Intelligent Edge Categories

2 TABLE OF CONTENTS

TABLE OF CONTENTS Audience ...... 1 Author’s Note ...... 2 Executive Summary ...... 5 Building An Intelligent Edge Strategy ...... 7 Edge Project Types ...... 7 The Early Adopter’s Problem ...... 9 Considering Life Cycles ...... 9 The Strategy Box ...... 11 Intro to The Intelligent Edge...... 13 What is The Edge? ...... 13 Why Location Matters ...... 15 Edge & Cloud ...... 15 What is the Intelligent Edge? ...... 16 Intelligent Edge Categories...... 17 01. Edge Computing ...... 18 02. Edge AI ...... 20 03. Smart Devices, Supersensors, Actuators ...... 22 04. Edge Data Management...... 24 05. Edge Infrastructure ...... 26 Edge Markets ...... 29 Horizontal Markets ...... 29 Vertical Markets ...... 30 Complementary Markets ...... 30 Venture Capital & Other Investments ...... 31 Intelligent Edge Models & Terms ...... 32 Clarification of Terms ...... 32 Edge Overview Diagram ...... 34 Intelligent Edge v. Cloud ...... 34 Device Edges v. Infrastructure Edges ...... 35 Device Edges ...... 35 Infrastructure Edges ...... 36 Homogeneous and Heterogeneous Edges ...... 41 Homogeneous Edge ...... 41

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Heterogeneous Edge ...... 42 Autonomy and Local Interactivity ...... 43 Device Edge Characteristics ...... 45 Planning Your Strategic Edge Initiative Using Lifecycles ...... 47 Intelligent Edge Life Cycles...... 47 Edge Data Management ...... 53 Strategic Edge Data Design ...... 53 Edge AI ...... 58 A Brief History of AI ...... 58 Categories of AI ...... 59 AI Companies...... 60 Executive Interviews ...... 67 Author ...... 69

AUDIENCE

This report should be read by CIOs, CTOs, CMOs, Ventures. We have interviewed dozens of executives, strategy executives, enterprise architects, and other salespeople, engineers, designers, and visionaries at technology executives and managers involved with the cutting-edge of this field. Mentions of companies in technology planning. It aims to demystify Intelligent this document are intended as illustrations of market Edge technology and help leaders and companies evolution and are not intended as endorsements or derive strategic value from its expansion. It is the result product/service recommendations. of over a year of research and reflection within RedChip

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AUTHOR’S NOTE

Years ago, I knew the chancellor of a major state university. Charlie, a former chemistry professor, was a brilliant guy. He worked on the first atomic bomb and loved to tinker with computers. He wrote and spoke about the Internet a lot. In the mid-1990s, he predicted that the Internet would become something like a worldwide, video-based CB radio. This seemed like an unusual prediction at the time, but watching my sons on Instagram this afternoon I am struck with how insightful it was. In 1998 I had the chance to talk

Charlie’s work had staying power like that. In the 1980s, with Steve Jobs after he’d come he wrote custom software for an apartment back and turned Apple around. I management company. The application, which ran on a Mac, handled rent roll and maintenance. In the early was there to help Telecom Italia try 2000s, this same company asked me to help them to do a deal with Apple, but after select new software. By that time, the company had grown and managed thousands of units. To my that business was completed I amazement, they still used Charlie's custom software couldn’t help asking a question. which had not been changed since the 80s. “Steve,” I said, “this turnaround at I recommended updating to a new, web-based system. As a self-proclaimed emerging technologist, I had no Apple has been impressive. But choice. At first the new software was not popular, but everything we know about the the users did eventually prefer it. In a few weeks, the new system began delivering measurable benefits. personal-computer business says

I learned through this experience that timing really does that Apple will always have a small matter -- a lot. It turns out this company was not late niche position. The network to adopt this software as I had originally thought. The timing was about right. Earlier efforts would have been externalities are just too strong to much too slow and expensive. Later efforts would have upset the de facto ‘Wintel’ standard. inconvenienced tenants, increased costs, and threatened the company’s viability. So what are you trying to do?

How should a company decide when to adopt a new What’s the longer-term strategy?" technology? As a strategist, the theme of “when (or if) to adopt a new technology” recurs frequently. You can't chase every new trend. Most emerging technologies simply are not worth chasing. They can end up making a mess of an otherwise well-run company. But some technologies are worth pursuing. Indeed, some technologies create so many opportunities that you have no choice but to embrace them.

The Edge is like this. It will be the largest and most influential technology of my lifetime. The lack of Edge technologies will be what later generations notice about our time; just as we notice what’s lacking in pictures of cities in the 1890s with horses and buggies, or of families in the 1930s gathered around vacuum- tube radios. Now is a time when computers and Artificial Intelligence (“AI”) don’t yet interact freely with us in all aspects of life. We live in the pre-Edge era where computer input is accomplished tediously with our fingers pecking away at screens and boards. This will all seem quaint and innocent in the future.

There is a lot of information available about the Edge. But this information, mostly in the form of reports, articles, and videos, is primarily focused on what Edge is -- trends, technologies, and features.

Instead, this report will focus on how Edge technology can be applied to business problems. It aims to help you answer the question, ”How can I use Edge to gain advantages and make money for my business?” He (Jobs) didn’t agree or disagree with my assessment of the market. Trends can be informative, but strategic advantages are not derived from trends. Strategic advantages are He just smiled and said, “I am derived from critical uncertainties -- who and what will going to wait for the next big win out in the market, what will your competitors do? Anticipating these uncertainties is half the battle. thing.” --Richard Rumelt But as Rumelt points out regarding Steve Jobs, the most effective strategies are also well-timed. Embracing Edge too soon will not provide an advantage. It will be a net cost to many companies and create misrule that ripples through those organizations for years. Adopting Edge technologies late will cause different problems. It will drag operations down and can cultivate bureaucracy. Companies will make this mistake too. Either mistake can put a company out of business. Getting it right matters.

The purpose of this report is to help you think through timing, technologies, and uncertainties so you can build an effective Edge strategy. I hope that by reading it, you will come away with a better idea of how to position your company to harness the full potential of the Edge.

Daniel Sexton, 2019

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EXECUTIVE SUMMARY

Disruption in business is accelerating. Half of the processing closer to points of origin. But it is more than companies listed on the S&P 500 will be replaced over this too. Compelling new business cases are emerging the next 10 years. In 1958, the average tenure of a as advances in sensor technology, improvements to listed company was 61 years; today it is 17 years; by Edge network infrastructure, and AI come together at 2027, it will be 12 years. Unlike the gentle movements a single point. of Smithian hand of old, today’s invisible hand is swift and exacting. Companies that are not prepared for new The Edge is certain to disrupt businesses and markets market swings quickly become irrelevant. in unexpected ways. Edge market projections already far outstrip Cloud. The Internet of Things (“IoT”) The Intelligent Edge (“Edge”) is the next such make- market alone, which is only a subset of the Edge, is or-break development. At a basic level, Edge enables projected to be between $442 billion and $1.2 trillion computer processing within or near a service point by 2022 (depending on how you define IoT) and is rather than in the Cloud. While this may sound like a growing at 29.4% per year— roughly double the size normal development in tech progress, it will alter life and growth of the global Cloud market. Edge AI, 5G, and business as we know it. New Edge capabilities are Edge infrastructure, Edge computing, new sensors and already ushering in a historic wave of technological devices, and Edge data management will all be innovations. Edge is transforming customer massive, growing markets over the next decade. experiences, redefining business and revenue models, and streamlining operations. Yet, market projections alone do not capture the essence of what the Edge will become. Edge will bridge The term “Edge” describes a physical location. It is the physical and digital realms in revolutionary ways. where computing resources are moved as close as Channeling its power will require radical, strategic possible to end users and devices. But, in some ways, thinking. ”Edge” is a misnomer. It is the Edge only with respect to centralized computing systems such as the cloud or Deriving value from Edge will be challenging. When enterprise data centers. Yet the Edge is everywhere. It cars can drive themselves and factories operate is where people and machines unite with the digital autonomously, entire cities will transform. The second- world. The Edge landscape includes our homes, bodies, order effects of such a historical revolution will change apparel, stores, factories, cities, streets, parks, how businesses operate and should not be buildings, hospitals, sports facilities, outer space, and underestimated. What will cities look like when parking unlimited other places and spaces, people and things, is not needed or can be moved further away? living and not. Increasingly, the Edge will be at the center of our lives. It was easy to predict mass car ownership but hard to predict Walmart. -Carl Sagan This report will explain why Edge is so revolutionary, how it will unfold in technical and social terms, and how There are definitive steps you can take to better align companies can harness its potential. Edge is often yourself to the opportunities that lie ahead. I hope this described as an extension of Cloud, but it is much more report will help you and your company navigate this than that. It is a revolution in AI distribution, edge amazing market revolution and ensure that the infrastructure, sensor technology, and unstructured invisible hand brings you along for the ride. data management. For many applications, executing AI in the cloud creates latency problems. Edge solves these problems by moving algorithms and data

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BUILDING AN INTELLIGENT EDGE STRATEGY

The purpose of this report is to answer two questions in detail:

What is the Intelligent Edge How can I use Edge to my (“Edge” or “the Edge”)? company’s advantage?

Before we dive into the details of what comprises Edge technologies (Question 1), let’s first consider how we might construct a strategy to take advantage of the Edge.

EDGE PROJECT TYPES To start, consider this question: Which of the following types 2, 3, and 4 have been increasing and are aimed best describes your company’s Edge initiative? at top-line growth. It is these latter three, especially innovation, that benefit from a more sophisticated 1. Improvement of operational efficiencies strategic approach. or reduction of expenses. 2. Scaling a current line of business. Since Carr’s article was written, IT has been 3. Entering new markets. increasingly relied upon to play a role in strategic 4. Innovation or development of a new corporate outcomes. Helping to increase revenues is a product or service. qualitatively different type of project than historical, cost-saving IT projects. But most IT projects already You may notice that these selections become fail today. So as IT projects become more progressively more difficult as you go from 1 to 4. sophisticated and centered on growth, strategy Historically, most IT projects have fallen into category becomes critical to the success of the company. 1. These projects help improve operations, lower expenses, and keep the business running smoothly. As IT becomes a profit center as well as a cost center, The other 3 types are intended to increase revenues. identifying which type of initiative you are undertaking becomes a critical part of building a strategy. Why do you need an Edge strategy? Categorizing project types helps determine when and how to undertake projects. Reducing operational costs Some argue that IT projects never actually provide an with technology is simpler and more straightforward advantage. IT, some have claimed, is a cost center; than creating growth. But with Edge technologies, and nothing more than a necessary cost of doing business. in fact any emerging technology, reducing costs can In 2003, Nick Carr wrote a pivotal article in The also be tricky. Finding ROI-based Edge business cases Harvard Business Review entitled, “IT Doesn’t Matter,” can be difficult for several reasons. To start, where he argued that IT provides only short-term implementation costs and timelines are hard to advantages: establish upfront. This makes it harder to predict how much value a project can provide. Also, early The trap that executives often fall into, however, is technologies generally haven’t had time to assuming that opportunities for advantage will be commoditize, so critical component costs are often still available indefinitely. In actuality, the window for high. gaining advantage from an infrastructural technology is open only briefly. -Nick Carr Revenue-generating projects (selections 2, 3, and 4) are not only more difficult and costly to implement but Certainly, projects of type 1 provide only short-term their success relies more on strategic approach. In advantages, if any. But, in recent years, projects of

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cost-reducing projects, what your competitors do A good strategy and approach can improve those odds. matters a lot less. But growth initiatives must take into The following two sections will help set the stage for consideration how technologies will change and how developing your overall Edge plan as you learn and competitors will act and react to your actions. explore what The Intelligent Edge can do for your Additionally, the technical implementation is also more company. difficult because, to be strategic, components must be implemented earlier in their life cycles which introduces complexities.

THE EARLY ADOPTER’S PROBLEM A few years ago, a Fortune 500 company began Should this project have been attempted? Or would it building a proprietary AI application. The new system have been better to wait until the technologies used some cutting-edge features, such as natural matured? language processing (“NLP”). After attending a particularly compelling conference, an executive was If you start a tech project too early, you can get finally sold on the benefits of building an internal Data leapfrogged by competitors who will face lower costs Science team. He hired a small team of engineers and and have access to better technologies later. Any data scientists to build custom algorithms and advantages may be short-lived, and it is not likely that applications for targeted business cases. When they any advantage will be gained. When future costs and began building this system, NLP Application success ratios are considered against ROI, it almost Programming Interfaces (“APIs”) on the market were always appears beneficial to wait, especially for larger not very good. They were too generic, and the results incumbents. were inaccurate. Considering all the variables, building their own solution seemed like the best option at the If you wait, however, many competitors will attempt time. innovative projects, and some will succeed. And some of those who succeed will parlay this success into long- Fast-forward a few years and the results of the project term advantages. Out of the initial large pool of have been mediocre. The system did provide value, but participants, a few will survive to challenge and even it was expensive to implement and it did not provide displace large incumbents. If you wait, you are likely to the lasting competitive advantage that was sought. lose the current advantages you have. Today, a better solution can be built at pennies on the dollar because much of what was developed internally This is the puzzle facing thousands of companies right is now available on the market in AI SaaS components. now regarding Edge projects. Do you dive into your For example, Cloud Service Providers (“CSPs”) now Edge project now or do you wait? And, if you do dive offer competitive NLP APIs. For instance, Google offers in, what do you attempt? Google Natural Language API. (Try out the tool here.) Amazon offers Comprehend. AWS offers vertical CONSIDERING LIFE CYCLES solutions, such as Comprehend Medical. You can even One way to address these concerns is to align customize your own Comprehend algorithm. Microsoft technology life cycles with the type of project being Azure offers LUIS. TextRazor is another option. attempted. Emerging technologies change quickly. As indicated above, they can even change within the Further, the market has disaggregated more generally timeline of a project. Additionally, not all technologies into discrete components, such as APIs for AI, cloud within the Intelligent Edge arena are emerging. Some infrastructure, SD-WAN and managed cloud databases. are mature and some are innovations that can precede This value-chain disaggregation was not anticipated emerging markets by years. So, it is important to while the system was being designed, so system determine the pace and acceleration of key components are coupled to outmoded infrastructure, components within their life cycles. Understanding how code, database, and algorithm choices which has components are likely to evolve goes a long way created technical debt. To take advantage of new APIs, toward producing a common-sense strategic plan. the code will need to be refactored. Also, since personnel resources were spread thin into areas that Consider the following chart which depicts 4 stages of the market would later address, the proprietary data the technology life cycle. Components start as design was less than optimal. So, the approach to innovations (lower left), and mature into products, and experimental modeling will need to be rethought and eventually become commodities and services (upper redesigned. right).

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With only one exception (covered later in the report), cycles. It was produced by considering a number of technological assets can only provide strategic value factors, such as supply chain disaggregation, adoption for a limited time as they move along this curve. The rate variables, and market fragmentation (CR4 ratio). key to a great strategy is to identify when, during the It approximates the maturity and strategic value of a course of its life cycle, a technology can provide a number of Intelligent Edge technologies. The S curve competitive advantage. Then you must ensure that this shows the path that technologies typically take on their aligns with the type of project you are undertaking. life cycle. The further a technology is towards the top right of the chart, the easier it is to both purchase and The chart below depicts one interpretation of where a produce. The upper right corresponds to products that set of Edge technologies fall on their respective life are most like commodities.

Note: These are estimations based on broad years ago. It is also easier to purchase edge sensors technological categories. When building your own with AI capabilities (and to have them designed) than strategic maps for an initiative, be sure to consider life it was just a few years ago. Deep learning algorithms, cycle stages, adoption curves, rates of adoption, such as NLP, can be accessed through third party APIs, disaggregation, componentization, and similar such as TextRazor. Developing proprietary algorithms variables. Finding the appropriate level of abstraction has also become easier with maturing languages (R, for technological components based on your project Python) and development platforms, such as takes some effort but can provide much insight. MathWorks. Additionally,you can outsource your deep learning development more easily than before. For example, application APIs are easier both to develop in-house and to consume compared with five

THE STRATEGY BOX As you consider Edge technologies that matter to your companies or organizations are not able to reproduce initiatives, you may find it useful to produce a similar those capabilities. chart. A project can be decomposed into technical components and each component can be mapped on Components that fall within a certain range on the life its life cycle. Technologies often mature in predictable cycle S curve are most conducive for providing ways so this exercise can be enlightening. strategic advantages. The green box approximates the stage at which companies benefit from custom- A blank chart that you can fill in is located in the building solutions with these components. appendix to this report. Remember, the lifecycle is only Technologies at this stage cannot be easily purchased an estimate. Techniques for how to estimate life cycles or outsourced, so a company’s execution capability are covered in more detail later in this report. relative to competitors can provide an advantage for some period of time. These solutions are sometimes Technology projects can help play a critical role in considered complementary assets. Over time, corporate strategic initiatives without the technology however, these advantages become necessary costs of itself providing the advantage. Yet some technologies doing business as lower-price products become do provide competitive advantages because other available on the market.

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As noted above, it is possible to start initiatives too in more detail later in the report.) That machine learn- early. Technologies that fall closer to innovations in ing (“ML”) is a rapidly maturing market is evidenced by their life cycle are often plagued by inefficiencies that the existence of Sigopt, a type of meta-API solution, add significant costs and time delays to commercial was co-founded by Scott Clark, a pioneer in AI solutions. The disillusionment with Big Data a few years research. The company provides an API that helps AI- ago (and IoT more recently) come to mind. If your based SaaS companies tune APIs. It provides a solution is intended to provide a strategic advantage, hyperparameter optimization solution that automates then starting earlier in the life cycle may be worth the model tuning to accelerate the model development risk. But the ideal time to custom-build solutions is later process and amplify the impact of machine learning than some organizations habitually start. models in production at scale. This process empowers developers to generate more high-performing models Of course, it is also possible to start innovative projects in production. too late. One common mistake I’ve seen recently is building AI and data science capabilities which are In other words, Sigopt enables the development of available as third party APIs. Machine learning is a sophisticated AI APIs for SaaS companies. NLP, text, rapidly maturing market. Performance or qualitative and video sentiment analysis, consumer behavior, and advantages gained from custom algorithm pricing algorithms are examples of functionality that is development can be quickly outpaced by the market. already rapidly commoditizing within SaaS markets. APIs should be considered first unless there are This puts pressure on internal algorithm development reasons other than functionality and performance that to keep pace with maturing components in the market. the effort is being pursued. The following graph considers maturity/prevalence and Algorithm development is proving to be a less effective the speed of evolution for a set of Edge components. defensible strategic approach than data design. In fact, The color and projected path indicate the position of algorithms are commoditizing and algorithm each component. Each dotted line represents how fast advantages are becoming reliant on specialized chips a component is expected to mature over a period of that are produced large incumbents. (This is covered time, in this case one year.

INTRO TO THE INTELLIGENT EDGE

WHAT IS THE EDGE? Over the past five decades, computing architectures and Java applets were popular in times of decentralized have favored either centralized or decentralized computing. Today, the cloud as a centralized core approaches. Mainframes with text terminals, Unix dominates the computing topology. Cloud has had a servers, and thin clients found favor in eras of big impact on applications, business models, and centralized computing. Fat clients, personal computers, businesses.

The pendulum, however, is about to swing. As massive  Micro Data Centers and pervasive as hyperscale cloud has become, the  The Internet of Things Edge will become far larger and more influential. The  5G Edge does not replace centralized cloud computing  Edge Devices, Sensors, Gateways though; it is a complement to it. Computing resources  Blockchain lie on a spectrum between the Edge and core. The  Operational Technology (OT) Edge will be rapidly expanding for at least the next decade.

The term “Edge” describes a physical location. It is The Edge is the decentralized, where computing resources are moved as close as possible to end users and devices. The Edge includes physical location where computing locations with harsh conditions, such as remote or resources are being moved. In outdoor areas with poor quality connections. It also general, it refers to devices or includes pristine environments with high-quality infrastructure resources. equipment. Moving resources to the Edge reduces latency and improves local interactivity. There are several advantages to this configuration.

An ecosystem of technologies works together to make this new generation of business cases at the Edge viable. This ecosystem includes (but is not limited to):

 Edge AI  Specialized AI Chips  Edge AI Algorithms  Edge Data Centers

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WHY LOCATION MATTERS

Providing interactivity and respon- Consider the Autopilot feature found in all siveness at the endpoint location is new Tesla cars (2019): critical to certain applications. To understand why, consider the case of Eight surround cameras provide 360 degrees of the autonomous vehicle. To function, visibility around the car at up to 250 meters of the vehicle needs to continuously monitor its range. Twelve updated ultrasonic sensors surroundings and react appropriately. A range of complement this vision, allowing for detection of powerful sensors generate data that must be both hard and soft objects at nearly twice the processed immediately. These sensors include infrared distance of the prior system. A forward-facing cameras, 4-D imaging radar, and LIDAR. radar with enhanced processing provides

additional data about the world on a redundant The cloud is far too slow to analyze information from wavelength that is able to see through heavy even a single onboard camera. A cloud approach would rain, fog, dust, and even the car ahead. involve sending the data from the car to a cloud service, such as AWS Rekognition. This will not work To make sense of all of this data, a new onboard because the cloud has a baseline network latency of computer with over 40 times the computing 70+ milliseconds which is restricted, in part, by the power of the previous generation runs the new speed of light in fiber and wire, and is bound by the Tesla-developed neural net for vision, sonar, and distance to the data center. radar processing software. Together, this system provides a view of the world that a driver alone Edge solutions solve this issue by moving the cannot access, seeing in every direction algorithms and processing close to the endpoint. This simultaneously, and on wavelengths that go far virtually eliminates concerns with latency, jitter, and beyond the human senses. - tesla.com/autopilot bandwidth and makes the business case viable. Improving responsiveness by moving computations and algorithms to the Edge is just one benefit of Edge applications.

EDGE & CLOUD

Edge and cloud technologies are not mutually exclusive. Rather, they are complementary. Cloud computing is the on-demand availability of computer system resources, such as data storage and computing power, without direct active management by the user. As such, cloud technology can be located anywhere including the edge. It just so happens that today cloud computing is primarily centralized, but this may not always be the case. Edge data centers (covered later in this report) are a type of cloud offering. Some cloud services which are centralized today will move to the edge for use cases in the future. This has already begun to happen with AWS Outposts, AWS Greengrass, Azure Stack, Google Anthos, and other technologies.

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WHAT IS THE INTELLIGENT EDGE? The Edge becomes intelligent when AI is combined with smart devices and powerful Edge computing.

Intelligent Edge benefits include:

 Improved local interactivity; this includes the number of communications and interactions between devices and other devices, and between devices and users or The Intelligent Edge machines; combines advancements in Edge  Handling large volumes of complex data; computing with emerging  Reduced latency and lower bandwidth requirements;  Lower cost; complementary technologies --  Reduced data duplication, data storage, and AI, machine learning algorithms, transmission redundancy; Edge computing, smart devices,  Improved reliability; Edge data centers and networks  Facilitates compliance with laws regarding data -- to provide value right at the transmission and storage;  Improved privacy and security; point of interaction.  Increasingly autonomous features and systems.

Intelligent Edge business cases rely on an ecosystem of technologies that span a number of markets. Below is an incomplete list of related, overlapping complementary markets:

MARKET CAGR FUTURE MARKET SIZE

Edge Computing 58.00% $28.84B (2022)

Edge Data Centers 13.00% $1.54B (2022)

Cloud 17.50% $331.20B (2022)

AI 37.00% $191.00B (2022)

5G 111.00% $202.00B (2022)

IoT Sensors 33.60% $22.48B (2022)

IoT 13.60% $1,200.00B (2022)

OT 6.70% $40.14B (2022)

5G WILL COVER

15% OF WORLD

POPULATION BY 2025

INTELLIGENT EDGE CATEGORIES CURRENTLY, THE INTELLIGENT EDGE HAS 5 CATEGORIES:

01 02 03 04 05

Smart Devices, Edge Edge Edge Data Edge Supersensors, Computing AI Management Infrastructure Actuators

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01. EDGE COMPUTING

In general, edge computing means decentralized computing. It covers a broad range of technologies, including:

Peer-to-peer computing: Peers are nodes that  AI chips; make a portion of their resources (such as processing  AI accelerators; power, disk storage, or network bandwidth) directly  Deep learning available to other participants, without the need for algorithms; central coordination.  Edge data centers and networks;  Micro data centers; Grid computing: A computer network in which each  Edge devices, sensors and actuators. computer's resources are shared with every other computer in the system. Edge Computing Benefits

Mesh computing: A local network topology in which  Improved local interactivity; the infrastructure nodes connect directly, dynamically,  Reduced latency; and non-hierarchically to as many other nodes as  Reduced bandwidth; possible and cooperate with one another to efficiently  Lower costs; route data from/to clients.  Reduction of data duplication;  Reduced data storage and transmission Fog computing: An architecture that uses Edge redundancy; devices for compute, storage, and communication  Improved reliability; locally which are then routed over the internet.  Improved compliance with laws governing privacy, security, data transmission, and Blockchain: a system in which a record of storage; transactions are maintained across several computers  Increasingly autonomous systems and AI; that are linked in a peer-to-peer network.

Content Delivery Networks (“CDNs”): A EDGE COMPUTING IS A DECENTRALIZED/ geographically distributed network of proxy servers and their associated data centers that provide high DISTRIBUTED COMPUTING MODEL WHERE availability and high performance by distributing the COMPUTING RESOURCES SUCH AS COMPUTE service spatially relative to end-users. AND STORAGE ARE MOVED AWAY FROM CENTRALIZED SYSTEMS AND BROUGHT Edge and Micro Data Centers: A smaller or CLOSER TO PHYSICAL LOCATIONS WHERE containerized data center that is designed for computer THEY ARE MORE USEFUL. workloads that are needed closer to endpoints.

FOG COMPUTING REFERS TO THE NETWORK Edge computing components exist on one half of the CONNECTIONS BETWEEN THE EDGE DEVICES Edge-cloud spectrum. Edge devices are on one end of AND THE CLOUD. FOG COMPUTING EXTENDS this spectrum and centralized cloud services are on the THE CLOUD CLOSER TO THE EDGE OF A other. The spectrum includes a growing ecosystem of NETWORK. SEE OPENFOG CONSORTIUM overlapping and often competing technologies.

Edge computing components are becoming more FOR COMMONLY USED TERMS WITHIN THE specialized. Increasingly, they are competing in narrow INDUSTRY, SEE: OPEN GLOSSARY OF EDGE verticals to address particular business cases. These COMPUTING. THE STATE OF THE EDGE components include: MAINTAINS AN EDGE LANDSCAPE MAP.

BLOCKCHAIN Blockchain is a relatively new type of edge computing  Payment processing; system. It may soon offer a range of innovative Edge  Monitoring supply chains; solutions.  Sharing data;  Weapon tracking; As a type of distributed, decentralized computing  Digital IDs; system, blockchain is a natural fit for the Intelligent  Storing of medical records; Edge. It is a powerful complement to other types of  IoT networks and security; Edge solutions because it is designed to be  Tracking prescription drugs; decentralized from the ground up.  Copyright protection;  Disaster recovery; Blockchain is a way of storing a list of records (blocks)  Identity management; such that they cannot be easily altered. It is often  Tax regulation; described as an open, distributed ledger. It uses digital  Stock trading; signatures and hash functions to ensure that a list  Tracing food supply chain; cannot be changed without majority consent. A peer-  Digital voting; to-peer network manages a blockchain. And it defines  Title transfers (real the common protocol that is used to communicate and estate, autos, land); to create new blocks.  Gaming.

Blockchain has gained attention as the underlying technology of cryptocurrencies. But there are several other potential use cases which include:

Notably, blockchain technology enables smart contracts.

Smart contracts are self-executing contracts with the terms of the agreement between buyer and seller being directly written into lines of code. The code and the agreements contained therein exist across a distributed, decentralized blockchain network. Smart contracts permit trusted transactions and agreements to be carried out among disparate, anonymous parties without the need for a central authority, legal system, or external enforcement mechanism. They render transactions traceable, transparent, and irreversible. - Investopedia

Smart contracts are beginning to gain traction. Blockchain will eventually be a powerful technical and Decentralized finance, or DeFi, applications are social component of applications. It elegantly challenging traditional Fintech solutions (see github disintermediates gatekeepers and obviates list). Digital collectibles, enabled by smart contracts, bureaucracy. However, blockchain is still a nascent and non-fungible tokens, allow users to collect unique technology. It has a long way to go before becoming a items that are provably scarce. This is unlike Beanie viable component of practical business applications. Babies, for instance, which can be counterfeit. As an Non-blockchain businesses should consider the costs infrastructure service, Filecoin allows users to get paid and benefits of using blockchain. Due to its newness, for hosting the storage of files for others. It is an it still carries considerable risks. alternative to cloud service providers, such as Amazon Web Services (AWS) and Azure.

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02. EDGE AI

AI is the defining characteristic of Intelligent Edge  Nvidia applications. AI makes split-second decisions at the  IBM point of interaction. Autonomous vehicles, AR/VR,  Xilinx medical devices, and real-time facial identification are  HiSilicon examples of business cases that require high-powered  AMD Edge AI processing.  Groq  Apple In the past, AI was centralized and software-focused.  NuvoMind2 AI algorithms were executed on general-purpose chips  Thinci that ran on a centralized server. But AI solutions are  Mythic AI moving to the Edge. Algorithms running on specialized hardware tuned for specific tasks are quickly becoming As Edge AI matures, both hardware and algorithms will the norm. continue to become specialized. An example of AI specialization is iPhone’s Neural Engine. In September As a result, a number of companies are building chips 2017, Apple released Face ID which unlocks the device. specifically designed to run AI at the Edge. This list The feature uses dedicated neural network hardware includes: that is powered by a custom-built chip for iPhone 8, 8  NVIDIA (Jetson) plus, and X. The A11 Bionic chip can perform up to 600  Intel (Movidius and Myriad) billion operations per second. The new A12 Bionic can  Alphabet - Google TPU1 do 5 trillion operations per second. There are faster,  Baidu non-mobile chips on the market, but the A series is the  Arm fastest mobile chip. As a comparison, Deep Blue, the  ViaTech IBM supercomputer that beat Gary Kasparov at chess  LG in 1997, achieved 11.38 gigaflops. The A12 is up to 90  MediaTek times faster at 1 teraflop.  SambaNova Without this specialized hardware, Face ID may not  Wave Computing have been a success:  Qualcomm  Graphcore The experiences we deliver through the phone are  Intel critically dependent on the chip… We couldn't have  Imagination Technologies done that[Face ID] properly without the Neural  Adapteva Engine. -- Apple VP, Tim Millet  Samsung

1 https://www.aitrends.com/edge-computing/ai-on-the-edge- 2https://www.forbes.com/sites/moorinsights/2019/06/03/novumind evolving-rapidly-with-specialized-chips/ -an-early-ai-chip-startup/#79056d515b54

Google also uses its own custom chips for machine AI algorithms are also maturing. For instance, image learning. The new Pixel phones process image data classification using CIFAR datasets began to exceed directly on the device. Nvidia makes high-end graphics average human capabilities in late 2014. For example, hardware and AI software platforms. Nvidia’s Drive PX certain algorithms are geared to ingesting a set of Pegasus is can process 320 million instructions per images and describing the contents. For example, second which helps handle inputs for autonomous Imagenet Image Recognition ranks humans against vehicles (lidar, radar, HD cameras, etc.). algorithms for identifying objects in images (below).

Source: https://www.eff.org/ai/metrics

AI algorithms are beginning to catch up to or exceed human capabilities. Here is a short list of tasks for which algorithms are currently being trained to compete with humans. Humans perform better at most tasks but the margins are decreasing.

 Written language;  Scientific and technical capabilities;  Reading comprehension;  Solving constrained, well-specified technical  Language modelling; problems;  Conversation;  Reading technical papers;  Translation;  Solving real-world technical problems;  Spoken language;  Generating computer programs from  Speech recognition; specifications;  Music information retrieval;  Answering science exam questions;  Instrumental tracks recognition;  Learning to learn better.

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03. SMART DEVICES, SUPERSENSORS, ACTUATORS

Since their inception, computers have been able to calculate and perform logic. In order to be useful, however, data had to be formatted and input into a computer. Now computers can not only see, smell, hear, feel, and taste, but also reason (to some extent) from raw sensory inputs. Recent advances in AI enable computers to accept all kinds of unstructured data and make sense of it. These capabilities open up great opportunities to help solve business problems.

For example, computer vision and auditory AI can be  Ground-penetrating RADAR (Wavesense) -- used to analyze a human’s: sees 9-10 feet underground at speeds up to 70mph  Facial expressions;  4-D imaging radar (Arbe Robotics) -- can  Vocal affects; create detailed images at distances of over 900  Body poses; feet.  Interpersonal distance;  Gestures; Other examples of sensors used for edge applications  Heart and respiration rate to infer emotions, include: mood, health, and honesty.  Quantum sensors; Not all sensors are advanced. For decades, a broad  High Resolution cameras; range of commodity sensors has been used in industry.  Ultrasonic sensors; Some examples are:  GNSS (Navigation);  IMU (Inertial Measurement Kit)  Pressure;  LIDAR (Luminar and Blackmore Sensors --  Temperature; acquired by Aurora Innovation and Analytics).  Humidity;  Motion detection; Recent innovations in sensor technology are creating  CO2; fascinating opportunities. Consider the Gravity Pioneer  Accelerometers; project. A consortium of scientific and engineering  Voltage. companies is developing quantum, cold-atom sensors. These sensors can detect and monitor objects under These basic sensors are an important component of the ground with incredible precision. The sensor uses the Intelligent Edge. Yet, powerful new types of rubidium atoms cooled by lasers to just above absolute sensors are emerging. Consider the following sensors zero. The atoms are propelled upward in a vacuum and that are being used in autonomous vehicles: then measured as they fall back under gravity.

 Far infrared cameras (FLIR, AdaSky, and Seek Thermal);

As an example of how sensor technology can be AI algorithms which scan three dimensional applied, consider the startup Doxel. Doxel uses robots representations of job sites to show daily progress. and drones to survey construction sites to inspect quality and find errors. The robots use sophisticated Sensors, new and old, are ushering in a new generation HD imaging and lasers to scan sites at all phases of of applications. These deal with everything from development. This replaces work done by humans complex machinery to simple, everyday tasks. Below which takes days or weeks to complete and lags are some additional resources for further information construction activity. The reports are generated using on the use of sensor technologies.

Drones are a rapidly growing technology with great potential for Edge applications. The number of drones in the United States is large and growing rapidly: 1,499,839 1,079,610 Drones Registered Recreational Drones Registered 416,210 158,554 Commercial Drones Registered Remote Pilots Certified

Autonomous Car Companies

Aptiv Waymo (formerlyGoogle Luminar Aurora (acquired self-driving car) Blackmore Sensors)

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04. EDGE DATA MANAGEMENT

Moving new sensing capabilities and cutting-edge AI be sent to a centralized into all areas of life and business means that data -- server, but most of it will not. lots of it -- will be generated everywhere. According to Data produced at the Edge the International Data Corporation (“IDC”), the sum of will be too large to be the world’s data will grow from 33 zettabytes in 2018 transmitted to the cloud. In to 175ZB by 2025.3 The IDC also estimates that there fact, recent projections will be 41.6 billion connected IoT devices, or "things," exceed the capacity of generating 79.4ZB of data in 2025. existing underground fiber. Consequently, it will be necessary to process most data This data will need to be managed and integrated at the source. Today, 10% of data is created and wisely. Data is quickly becoming the primary technical processed outside of a centralized data center or cloud. strategic asset. Some data generated at the Edge will By 2022, Gartner predicts this figure will reach 75%.4

By way of example, consider the amount of data generated by these two systems

19TB per hour 70TB per hour Autonomous car Commercial Aircraft

As autonomous and semi-autonomous vehicles fill the benefits over traditional, cloud analytics systems and roadways in coming years, the amount of data data warehouses. First, it makes it possible to obtain generated from them will be astronomical. But real-time insights from devices for visualizations and automobiles and aircraft comprise only a small fraction insights at the Edge. This means that machines and of the overall Edge data that will be produced. Edge users can benefit from immediate data analysis at the devices of all kinds, including IoT and OT devices, will location where the data is being generated. The produce massive amounts of disparate data from second benefit is that it reduces the amount of data billions of endpoints. which travels to the cloud.

EDGE ANALYTICS Edge analytics is a large market with staid industry players such as Microsoft, IBM, and Amazon Web Edge analytics is an approach to data collection and Services. However, smaller, innovative companies have analysis in which automated analytical computations entered this space as well. As an example, here are are performed on data at the Edge. The purpose of three companies which are addressing ancillary Edge analytics systems is to derive actionable insights from markets in new ways: data. Analyzing data at the Edge provides two primary

3 https://www.seagate.com/our-story/data-age-2025/ 4 https://blog.seagate.com/vision/edge-computing-and-the-future- of-the-data-center/

Edge Intelligence: Provides an analytics software platform for mobile Edge computing that processes data in real-time and provides insights into geographically distributed Edge data from devices such as network servers, routers, and threat intelligence platforms.

Phizzle: Builds MEC marketing solutions by using Edge computing to help organizations combine their business intelligence and machine learning to improve everything from SMS marketing to social data visualization. The Charlotte Hornets use the organization’s Edge computing offering to compile and distribute records and data for the team's millions of fans. This helps eliminate duplicate records and provides real-time marketing data.

Foghorn: Develops Edge intelligence software (real- time equipment insights to reduce processing and storage costs) for Industrial IoT (“IioT”) in sectors ranging from oil and gas to smart buildings and manufacturing.

The following is a short list of innovative edge analytics companies which are attacking some other interesting verticals.

 Databricks  Aruba Networks  Lookout  Redis Labs  MapR  SparkCognition  Element AI  Cardlytics

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05. EDGE INFRASTRUCTURE

Edge infrastructure comprises the service provider side rooms, and in other locations where space is at a of the last mile network. This includes Edge data premium. centers and micro data centers as well as multi-access edge computing (“MEC”) and Narrowband IoT (“NB- MODULAR EDGE DATA CENTERS IoT”). For Edge deployments, many enterprises do not have available space. A common solution is to retrofit a small Average network latency to a cloud service from an area into a Micro Data Center. However, since these Edge device is around 70-80 milliseconds. This means areas are not designed to house IT equipment, they that a round trip call to a cloud service does not can have several concerns including high cost, power typically execute faster than approximately 200 availability, security, cooling and other environmental milliseconds. Edge infrastructure, such as edge data controls. centers, can offer round trip speeds under 10 milliseconds. This makes edge infrastructure a critical Modular Edge Data Centers solve this problem by architectural component of many Edge business cases. offering a prefabricated structure that is designed to house critical IT equipment. These systems are easy to EDGE DATA CENTERS deploy and cost between 20-30 percent less than Edge data centers (“EDCs”) are a subcategory of Edge standard data centers. Cooling, power, security, computing. EDCs are similar to content delivery environmental controls and fire protection are built into networks (“CDNs”). EDCs are designed to be a faster, the units. light-weight alternative to cloud infrastructure. They can be used for caching, processing, storage, and CSP ON-PREMISE HYBRID CLOUD (ITAAS) related functions. EDCs are also designed to CPSs offer their own variations of edge data centers complement existing cloud or colocation deployments. which facilitate hybrid cloud solutions. Hybrid cloud is They can be located on-premise or within 5-10 miles of an approach to enterprise architecture that involves users and endpoints. Edge data centers are a type of running workloads across Edge and Cloud cloud offering that is closer to endpoints and the “last infrastructures. CSPs are now offering on-premise mile” of delivery. hardware solutions with many of the same service offerings that are found in the centralized cloud. MICRO DATA CENTERS Prominent examples include AWS Outposts, Azure Micro Data Centers (“MDCs”) are smaller than EDCs. Stack, and Google Anthos. These offerings vary quite a They are about the size of gun storage cabinets. bit between providers, but, in theory, hybrid on- Typically, they are 3-6 kilowatts in capacity per cabinet. premise solutions are excellent for Edge applications MDCs are deployed in offices, stores, warehouses, that require low latency or have data residency factories, within telecommunications computing concerns. Since hardware is deployed on-premises, it

can be customized to provide fast and specialized edge (ISG) within the European Telecommunications data processing per use case. Standards Institute (“ETSI”).

MULTI-ACCESS EDGE COMPUTING (MEC) NARROWBAND IOT (“NB-IOT”) Multi-access Edge computing (“MEC”) provides cloud Narrowband IoT is a Low Power Wide Area Network computing services in an ultra-low latency, high (“LPWAN”) radio technology standard developed by bandwidth environment. MEC is located physically at 3GPP to enable a wide range of cellular devices and the edge of a cellular network[1][2]. It works in services. NB-IoT offers improved indoor coverage and conjunction with mobile base stations but can be used can support a large number of lightweight devices. with any network including LTE and 5G. Operators may These devices are typically quick, low cost, and low authorize third parties to use their Radio Access power. NB-IoT can be deployed within an LTE carrier Network (“RAN”). This allows third parties to deploy (in-band), within an LTE carrier’s guard-band, or as a applications. MEC is an Industry Specification Group standalone in a dedicated spectrum.

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EDGE MARKETS

The term Intelligent Edge describes how a technical solution is designed. It is not a single market or specific application. There are numerous Edges, Edge components, and markets. To help describe Edge initiatives, the following sections explore market categories.

HORIZONTAL MARKETS Some large providers serve horizontal markets. These include cloud service providers, telecoms, and data  Dell IoTech center providers. Many of these providers have begun  Dell & Intel: Foghorn adding Edge products and services to their offerings.  SAP Edge Services

For instance, Amazon Web Services offers AWS Greengrass, AWS IoT Core, and AWS Outposts.  NVidia EGX Microsoft, on the other hand, offers Azure IoT, Azure  Edge and Micro Data Centers IoT Edge, and Azure Stack.  Cisco The following list includes a number of large providers/  Juniper Networks solutions addressing horizontal markets:  Arista Networks  Huawei  AWS IoT  EdgeConneX  AWS IoT Core  Flexential  AWS Greengrass  Equinix  Microsoft Azure IoT  Vxchng  Azure IoT Edge  365 Data Centers  IBM IoT  Google Cloud IoT *Note: Cisco, Dell, and Microsoft formed OpenFog  Google Edge TPU Consortium.  Cisco Edge Computing Infrastructure Note: Also consider the other horizontal markets for  General Electric Edge Computing components within the edge ecosystem.  Hewlett Packard Enterprise Edge Computing Microcontrollers, microprocessors, sensors, gateways,  HPE: The Edgeline Converged Edge and similar parts are relatively mature horizontal Systems (Model EL 1000) markets which affect Intelligent Edge solutions.  AT&T Edge  Intel Edge Example Horizontal Specialty Companies: Rigado: IoT data solutions for smart, connected environments. The Cascade- 500 IoT Gateway product provides Edge connectivity to sensors, devices, and the cloud and was one of the first to include AWS Greengrass. The company’s gateway products help reduce latency and also allow a range of endpoint connectivity options including Bluetooth and LTE. Clear Blade: IoT Platform. Edge computing software lets businesses securely run and scale IoT devices in real-time.

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VERTICAL MARKETS Consumer products and end-to-end solutions tend to address market verticals. Examples are too numerous to list but include:

 Energy & utilities  Olli  Healthcare  Autonomous Trucks (McKinsey Report)  Agriculture  Uber Advanced Technologies Group  Manufacturing (defunct)  Transportation & logistics  TuSimple  Railway Cargo  Retail  Hitachi  Data Centers  GE  Wearables  Autonomous Buses  Smart Cities, Smart Home, Smart Buildings

COMPLEMENTARY MARKETS Intelligent Edge business cases rely on an ecosystem of technologies. These technologies span several markets. Below is an incomplete list of related, overlapping complementary markets:

MARKET CAGR FUTURE MARKET SIZE

Edge Computing 58.00% $28.84B (2022)

Edge Data Centers 13.00% $1.54B (2022)

Cloud 17.50% $331.20B (2022)

AI 37.00% $191.00B (2022)

5G 111.00% $202.00B (2022)

IoT Sensors 33.60% $22.48B (2022)

IoT 13.60% $1,200.00B (2022)

OT 6.70% $40.14B (2022)

VENTURE CAPITAL & OTHER INVESTMENTS Intelligent Edge investments comprise several As of this writing, early-to-medium stage investments markets. A non-comprehensive list includes: were highest in AI. In fact, in 2018, almost $100B flowed into AI with $9.9B going to early-stage AI  Edge AI (Artificial Intelligence) startups. Contrast that with hardware-heavy areas  AI chips (such as IoT devices, OT devices, and networking  Edge AI algorithms hardware) which are experiencing a steady decline in  Edge Data Centers early and later-stage investments.  Micro Data Centers  Multi-Access Edge Computing Meanwhile, larger, established companies are investing  IoT in the “Intelligent Edge.” HPE and Microsoft  Platforms announced $4B and $5B investments respectively.  Analytics Likewise, the telecom industry is investing in 5G.  Data Management Massive MIMO and small cell/dense deployment  Security infrastructure is reaping the majority of the effort, but  5G software-defined (“SD”) solutions are also being  Edge Devices, Sensors, Gateways explored as they tend to reduce generation cycles from  Blockchain years to weeks.

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INTELLIGENT EDGE MODELS & TERMS

The Intelligent Edge is a broad term. It can help to have descriptive models that can guide conversations and planning efforts. The following sections provide a basic framework to help guide edge planning efforts.

CLARIFICATION OF TERMS EDGE & FOG COMPUTING INTELLIGENT EDGE V. INTELLIGENT MESH “Intelligent Mesh” is a subtype of edge computing. It is Fog computing is a system-level horizontal included within the scope of the Intelligent Edge. It architecture that distributes resources and services uses a less structured architecture than does traditional of computing, storage, control, and networking edge computing (or it can even use an unstructured anywhere along the continuum from Cloud to architecture). Typical Edge architectures have Things. - OpenFog Consortium structured, defined layers with clear responsibilities. Dynamic mesh architectures enable flexible and responsive Edge systems in a less organized way. The difference between Edge and fog depends partly on whom you ask. The terms are often used UBIQUITOUS COMPUTING, PERVASIVE interchangeably. In general, Edge refers to a location. Fog, a play on the term “cloud,” refers to a broader IT COMPUTING, INTERNET OF THINGS, AND architecture. AMBIENT INTELLIGENCE For the last 25 years, technologists have been The fog architecture is growing outward from predicting the “third wave of computing.”5 The first centralized cloud services. Fog mirrors the wave was mainframe or centralized computing. standardized, scalable model of cloud computing. It Mainframes facilitated a centralized “many people to extends the standardized services of cloud computing one machine” model. The second wave was personal to the Edge. Fog includes the space towards the Edge computers. PCs brought about the “one person to one that is not cloud. machine” model. The third wave, demarcated by pervasive, tiny computers and sensors, was intended The OpenFog Consortium includes Cisco Systems, to be a “many people to many machines” model. Intel, Microsoft, Princeton University, Dell, and ARM Holdings. According to OpenFog, fog computing always The terminology used to describe the third wave of uses Edge computing. Edge computing may or may not computing came out of funded research labs. These use fog computing. Additionally, fog includes the cloud terms include: whereas Edge does not.  Ubiquitous computing  Pervasive computing  Internet of Things  Ambient intelligence In February of 2017, OpenFog released its reference architecture for fog computing. Mark Weiser, the chief technologist of Xerox Parc, coined the term “ubiquitous computing” in 1988. He

used it to describe a paradigm that would overtake PCs. His vision consisted of tiny devices embedded in

5 https://www.sciencedirect.com/science/article/pii/S026736491500 1144

things in the physical world. These devices For the purposes of this report, “third wave of communicate and inter-operate via new wireless computing” terms overlap with and are contained communications technologies. 6 Notably, he defined within the Edge. They define different ways of this type of computing as “distributed, unobtrusive, describing Edge systems. Ubiquitous and pervasive and context-aware.7 computing describe Edge applications that are anywhere and everywhere. Ambient intelligence The more recent term, “ambient intelligence,” was describes systems that are both sensitive to and coined in the late 1990s by Eli Zelkha and his team at responsive to human beings. IoT systems can be either Palo Alto Ventures. It has been defined as “...an or both. The defining characteristic of an IoT system is emerging discipline that brings intelligence to our asset tagging. Some sort of asset or thing is monitored everyday environments and makes those environments and analyzed. sensitive to us. Ambient intelligence (AmI) research builds upon advances in sensors and sensor networks, EDGE INCLUDES IOT, OT, IT pervasive computing, and artificial intelligence.”8 Other popular terms related to Edge include the following technologies. The term “Internet of Things” is thought to have been coined by Kevin Ashton at Proctor & Gamble’s  The Internet of Things (IoT) Auto-ID center. Early IoT definitions centered around  Operational Technology (OT) radio-frequency identification (“RFID”) and tagging  Information Technology (IT) “things” so that computers could manage them. It has since evolved into a broader definition. The global IoT The scope and definition of these terms overlap quite market, as it is currently defined, is projected to be a bit, but each one is a subset of Edge. valued over one trillion dollars by 2022.

6 https://dl.acm.org/citation.cfm?id=329126 8 https://www.sciencedirect.com/science/article/abs/pii/S157411920 7 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2613198 900025X

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EDGE OVERVIEW DIAGRAM

Heterogeneous

Edge Device Sensors/ Edge Gateway Actuators Device Edge Data Center Homogeneous

Sensors/ Edge Actuators Device Edge Device Gateway Micro Data Center

DEVICE INFRASTRUCTURE CLOUD / CORE EDGE EDGE INTELLIGENT EDGE V. CLOUD

INTELLIGENT EDGE

CENTRALIZED CLOUD

Edge Data Center Edge Device

Sensors/ Edge Gateway Actuators Device Micro Data Center

“Edge” and “cloud” are not opposing terms.  Cost constraints vary widely and compel Nevertheless, Edge systems do have different thoughtful designs. attributes than cloud systems. Here are some of the  Power management can be a concern. key differentiators of Edge systems when compared  Data tends to be more complex and with traditional, centralized cloud systems: heterogeneous.  Data generated at the Edge can be massive --  Scale and diversity of devices, data, and most of it will not be stored or transmitted. protocols is far larger.  AI/ML-specific hardware is a design  Architectural implementations tend to be differentiator. idiosyncratic, highly specific, and coupled to specific use cases.

 New tools, development environments, and  Devices can run on an OS (Linux), VM, or ecosystems add to application development bare metal. costs.  Common protocols (HTTP, TCP/IP) may or  Risk management and security are more may not be used. complex. For example:  Solutions are highly specific at the device  Locations sometimes share legal domain level. and precedents have yet to be established.

DEVICE EDGES V. INFRASTRUCTURE EDGES There are two Edges-- Device Edges and Infrastructure Edges.

DEVICE EDGE INFRASTRUCTURE EDGE CLOUD / CORE

Edge Data Center Edge Device

Sensors/ Edge Gateway Actuators Device Micro Data Center

DEVICE EDGES

Device Edges interact with users and machines.  Run operating systems (Linux), VMs, Devices include not only mobile phones, laptops, and containers, or bare metal; tablets, but also less conventional devices, such as  Send data via application level or lower OSI connected automobiles, airplanes, refrigerators, and levels; industrial motor sensors. The device edge includes IoT,  Several protocols; HTTP, XMPP, CoAP, MQTT, OT and IT devices. AMQP, VSCP, DDS, STOMP;  Produce various types of data, including: Edge devices can perform several functions. These  Standardized text data, such as JSON; include sensing, sending data to other devices and  Digital video, image and audio data; gateways, running algorithms, caching and analyzing  SCADA data; data, actuating and controlling machines, and running  Analog data; operating systems, containers, and VMs. In practice,  Binary data. Edge devices can be managed by cloud services using Amazon Web Services (Greengrass, IoT), Azure (IoT), Examples of Edge devices include simple sensors, such and other providers. as these temperature sensors.

Characteristics of Edge devices:  Single-function (temperature sensor) or multi-

function (mobile phone);  Send data via radio frequencies or wire;

 Battery or electric line powered;

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INFRASTRUCTURE EDGES

Infrastructure Edges are large-scale facilities operated as well as tier-2 markets like Pittsburgh or St. Louis. by service providers and network operators. These Primarily, this is because EDCs are much larger and facilities are located within the “last mile network.” provide significantly more computing power than They are centered between Device Edges and networks made up of Edge devices. centralized clouds or data centers. EDCs are evolving to handle complex, distributed Physically, the Infrastructure Edge is located within a architectures by supporting specialized accelerators. few miles of devices. It primarily serves to reduce For instance, Microsoft is investing at FPGA AI latency and improve bandwidth. Near-zero millisecond accelerators at the Edge.9 The NVIDIA EGX platform latency can be handled at the Device Edge. But “enables companies to perform low-latency AI at the medium-range latency requirements can be handled at edge — to perceive, understand and act in real time on the Infrastructure Edge. The Infrastructure Edge continuous streaming data between 5G base stations, provides a balance between latency and resource warehouses, retail stores, factories and beyond.” 10 density. It is a middle ground between Edge devices Cisco, Juniper Networks, Arista Networks, and Huawei and a centralized cloud. are also making strides in Edge-based data center and software-defined networking. Open standards projects are helping to create an Edge supply chain for deploying and managing Azion operates more than 30 Edge network data infrastructure. Examples include: centers worldwide with 70 slated to open over the next few years. The company helps businesses build  Open Compute scalable and secure serverless applications at the Edge.  Open 19 The Edge products connect to cloud service providers and facilitate app-building which decreases latency EDGE DATA CENTERS times for content downloading, personalized security Edge Data Centers (EDCs) act like mini-clouds (fog) building, basic connectivity, and other activities. and provide substantial latency improvements over centralized cloud services with similar availability (modified ANSI/TIA-942). Designed to complement

existing cloud or colocation deployments, EDCs can be located on-premise or within a few miles of users. On- prem Edge data centers are often connected directly through a private, high-bandwidth network and can also be wired directly to a LAN through fiber optic cabling. Off-prem EDCs are spread across cities and suburbs at 5-10 mile intervals.EDCs are much smaller than cloud data centers (between 50 to 100 square feet) and are 50-150 kilowatts in capacity. Cloud data centers, on the other hand, are 25-35 megawatts, have up to 80,000 servers and can take up hundreds of thousands of square feet.

Sub-10 millisecond latency will be common for EDCs located in tier-1 markets such as New York and Atlanta

9 https://www.datacenterknowledge.com/edge-computing/why- 10 https://nvidianews.nvidia.com/news/nvidia-launches-edge- microsoft-betting-fpgas-machine-learning-edge computing-platform-to-bring-real-time-ai-to-global-industries

Edge Micro, for its part, provides Edge colocation data  Juniper Networks centers. Data centers are carrier neutral, modular data  Azion centers. They use a patent-pending technology that  Edge Micro the company calls Edge Traffic Exchange (“ETX”). ETX  Arista Networks offers cloud-like services (like compute, storage, and  Huawei network resources) at the Edge. The data centers offer  Nokia a single source for MNOs, ISPs, and content providers.  EdgeConneX  Flexential Companies in the Edge Data Center market include:  Equinix  Vxchng  Hewlett-Packard  365 Data Centers  Eaton Corporation  IBM Corporation EDCs are deployed in a range of close-to-endpoint  Hitachi Vantara locations and can be custom-built at a specific location.  Rittal They can be deployed within buildings and rooms not  Vertiv originally designed to support data center equipment.  Flexential Corporation They can even be deployed outdoors. Examples  Schneider Electric include:  365 Operating Company  Vapor IO  Modified cell tower shelters  Panduit Corporation  Modified cabinets  Fujitsu  Drop and plug shelters  Cisco  Drop and plug cabinets  ACI Anywhere  IDF closets in a building  Data Center Anywhere  Co-located in an office or data center  Hyperflex Anywhere  In box on a light pole.

Edge Data Center Google Hyperscale Cloud Data Center

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MULTI-ACCESS EDGE COMPUTING (MEC) Vehicles , Over-The-Top (OTT) video streaming, or online games. Edge compute will become more Multi-access Edge computing (“MEC”) provides cloud important as 5G matures, but there are near-term computing services in an ultra-low latency, high challenges to MEC payoff. bandwidth environment. MEC is located physically at the edge of a cellular network.[1][2] It works in If telco’s see themselves as the next generation conjunction with mobile base stations but can be used providers of Edge computing, then they should with any network including LTE and 5G. Operators may consider these core issues. authorize third parties to use their Radio Access Network (“RAN”). This allows third parties to deploy  The payoff is uncertain for software-defined applications. MEC is an Industry Specification Group data centers based on a future 5G standard. (ISG) within the European Telecommunications  Can applications work natively (like they do on Standards Institute (“ETSI”). cloud today) on a new MEC architecture?  The real user need -- if faster and less latency In 2013, Nokia Siemens introduced MEC, originally are measures – how much better is the called Mobile Edge Computing. MEC is a proprietary experience? technology that facilitated Intelligent Edge applications  How does aggregation of mobile data called “Liquid Applications.” This technology combined baseband units (“BBU) managed into one cloud and cellular network capabilities into a platform master controller matter? which provided APIs and compute resources to  Endpoint security will be key for data developers. Since that time, Nokia has opened MEC to protection -- is that a greater threat and at a larger audience by promoting it as an open standard what cost? to be supported by other manufacturers. In 2015, IBM,  Will device microcontrollers drive Edge NEC, Vodafone, NTT Docomo, Orange, and Nokia computing as opposed to the MEC at the partnered to create an industry specification group network? within ETSI to broaden the use of MEC.

In 2017, ETSI renamed Mobile Edge Computing to Multi-Access Edge Computing. This reflects a wider definition as Edge computing includes wired networks as well.

MECs provide functionality that is similar to traditional CDNs but with the added capability of accelerating data transfer. Some common use cases include:

 Data caching;  Augmented reality (“AR”);  Mixed reality (“MR”);  IoT;  Analytics;  CDN-type functionality.

MEC implementations are in the early stages, moving from concept architectures into proof-of-concept trials. The primary value appears to be latency improvement for devices or applications that could use increased response times and less jitter, such as Autonomous

Example MEC companies: MICRO DATA CENTERS Micro Data Centers (“MDCs”) are smaller than EDCs. Saguna is a company that provides MEC for mobile They are about the size of gun storage cabinets. operations and enterprises. The Saguna Edge Cloud Typically, they are 3-6 kilowatts in capacity per cabinet. creates edge-cloud-computing environments inside the MDCs are deployed in offices, stores, warehouses, access network that let 5G features function over factories, within telecommunications computing existing 4G settings. Additionally, the company’s Multi- rooms, and in other locations where space is at a Access Edge Computing for enterprises helps a variety premium. of businesses implement edge data processing tools for the growing number of IoT devices in use.  Canovate Group  Dataracks Vapor IO has existing data centers located at the edge  Hewlett-Packard Enterprise Company of cell phone towers which reduces endpoint latency.  Delta Power Solutions The company uses colocation facilities to provide cloud  Attom Technology services at the edge of wireless networks. In February  Eaton Corporation 2019, Vapor IO and bare metal automation platform  Advanced Facilities, Inc. Packet unveiled the first two live Kinetic Edge sites in  Dell Inc., Dataracks Chicago.  Huawei Technologies Co NARROWBAND IOT (“NB-IOT”) MODULAR EDGE DATA CENTERS Narrowband IoT is a Low Power Wide Area Network Conversely, the modular edge data centers contain (“LPWAN”) radio technology standard developed by multiple MDCs with built-in infrastructure in an all- 3GPP to enable a wide range of cellular devices and weather container that are pre-equipped with services. NB-IoT offers improved indoor coverage and communication and backup power. These ready-to- can support a large number of lightweight devices. deploy modular data centers are installed at remote These devices are typically quick, low cost, and low sites/locations. They address the bandwidth and power. NB-IoT can be deployed within an LTE carrier latency issues by connecting to nearby regional data (in-band), within an LTE carrier’s guard-band, or as a centers where needed, thereby enhancing the user standalone in a dedicated spectrum. experience. Also, such installations are at times deployed to mission-critical operations.

Edge technologies, such as wireless medical and health devices, large industrial motor and pump sensors, and smart farming equipment, lack the necessary compute and power capabilities to handle large and complex data. Modular edge data centers are being deployed close to these facilities to boost hyper-local processing power at the edge.

Typically the size of a truck trailer or shipping container, modular edge data centers are being used to help commercial facilities, mobile network operators (“MNOs”), and service providers augment capabilities

near the edge.

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CSP ON-PREMISE HYBRID CLOUD (ITAAS) CPSs offer their own variations of edge data centers which facilitate hybrid cloud solutions. Hybrid cloud is an approach to enterprise architecture that involves running workloads across Edge and Cloud infrastructures. CSPs are now offering on-premise hardware solutions with the many of the same service offerings that are found in the centralized cloud. Prominent examples include AWS Outposts, Azure Stack, and Google Anthos. These offerings vary quite a bit between providers, but, in theory, hybrid on- premise solutions are excellent for Edge applications that require low latency and have data residency concerns. Since hardware is deployed on-premises, it can be customized to provide fast and specialized edge data processing per use case.

In the following section, we will discuss multiple models that will help the discovery process for strategic analysis and discussions.

HOMOGENEOUS AND HETEROGENEOUS EDGES Edge landscapes fall into two primary categories -- homogeneous and heterogeneous.

Heterogeneous Edge Homogeneous Edge

 Manufacture SKU-based  Mobile Devices Homogeneous IoT Solutions  Autonomous Vehicles

 IIoT Specific Business  Large Industrial Asset Heterogeneous Case Mapping

ENVIRONMENT Low High

NUMBER OF ENDPOINTS

HOMOGENEOUS EDGE Homogeneous Edges provide highly scalable delivery platforms. As an example, the mobile device is the Homogeneous Edges are associated with widely world’s largest, Homogeneous Edge delivery platform adopted products and platforms. Mobile devices and for both hardware and software. As of October 2019, smart automobiles are examples of pervasive, there were 5.13 billion mobile devices worldwide.11 The homogeneous Intelligent Edge platforms. tremendous popularity of Homogenous Edges has spillover effects. For instance, manufacturing AI-

11 https://www.bankmycell.com/blog/how-many-phones-are-in-the- world

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optimized chips, such as the A12, requires significant complexity and heterogeneity of many Edge solutions new fab manufacturing capabilities that can be used to will make this difficult to replicate. However, there will create chips for other products. be Edge markets that commoditize both in vertical and horizontal markets. Cloud service providers are offering Automobiles are another great example of common easy-to-use cloud-like Edge services. These are called Homogenous Edge devices, with over one billion Fog. AWS Outposts is one example. Vertical markets currently in existence. Smart cars, in particular, have with tailored solutions built for specific use cases will numerous sensors (LiDAR, Radar, Sonar, GPS, expand. IoT and OT, which are both parts of the edge, cameras)12 and produce significant amounts of data. will merge with enterprise architectures. Estimates range from 1.4-19 terabytes per hour. These figures will grow exponentially as smart cars replace HETEROGENEOUS EDGE older, lower-tech models. Heterogeneous Edges are associated with highly These autonomous automobiles are nothing short of diverse environments as they support a wide range of mobile data centers. They require large compute and device types. They are also defined by complex or storage capacity. In fact, in order to handle all this data irregular network and infrastructure services, complex and processing, Tesla developed its own purpose-built, or uneven physical environments, diverse or difficult to self-driving chip. It began shipping in the Models S and manage data, unpredictable mobile or stationary X in March 2019. deployments, and other variables which discourage in- scope scalability and smoothness. Industrial plants and Any widely-available, uniform product or thing has the manufacturing facilities are common examples where potential to become the primary delivery platform for a complex, heterogeneous environments can be Homogeneous Edge use case. Examples include: deployed.

 Houses For instance, IoT business cases within a factory or  Shoes manufacturing plant are often heterogeneous.  Gaming consoles Mapping the assets within a large industrial factory is  Medical devices just one application for this technology. Large factories  Mattresses have hundreds of thousands of SKUs in operation.  Refrigerators Bringing even a tiny fraction of these online can be a  Boats complex and unpredictable endeavor. Heterogenous Edge projects often do not scale well in this Automobiles, for example, are excellent homogeneous environment. Edge platforms for a new generation of applications, such as insurance apps that gather real-time driving A number of business cases within large factories are information and provide emergency response. homogeneous, however. For instance, motor manufacturers, such as Nidek and ABB, have begun CONSISTENCY CAN BE SCALED building intelligent sensors and algorithms into their motors. Since these add-ons are tied to a specific SKU The incredible growth of cloud or categories of SKUs, the deployment is computing can be attributed both to homogeneous. its consistency of service offerings and the economies of scale in building out large data centers. The

12 https://info.microsoft.com/rs/157-GQE-382/images/K24A- 2018%20Frost%20%26%20Sullivan%20- %20Global%20Autonomous%20Driving%20Outlook.pdf

AUTONOMY AND LOCAL INTERACTIVITY Other important distinguishing factors for Intelligent Edge business cases are levels of autonomy and local interactivity.

Multi-Drone Video Surveillance Complex Large Factory IIoT Solution Technology

Simple IoT Sensors Virtual Assistant

Low High

LOCAL INTERACTIVITY LOCAL

AUTONOMY

Autonomy refers to the capability  devices and other devices at the edge; or of the system for independent  devices and other things or humans at the computation and decision making at edge the edge. Systems with high autonomy depend less on a Complex local interactivity consists of numerous, centralized server. They require less sequential, or concurrent steps and/or a higher number real time interaction. Decision-making behavior (e.g., of sensors. High interactivity requires stateful (usually machine learning, narrow AI, algorithms) is placed at concurrent) processing between multiple inputs and/or the edge. endpoints and devices. Simple levels of local interactivity require fewer or slower steps. Lower levels of autonomy require interaction with a central server. They may have an intermittent or Autonomous cars and networked drone cameras are infrequent internet connection. And they follow a examples of products with high levels of autonomy and simpler set of rules (like ants). local interactivity. A car, for instance, must make continuous millisecond decisions which require narrow Local interactivity refers to the AI and sophisticated sensors such as LIDAR and number of and level of cameras. communication and interaction between:

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The Princeton Edge Lab is conducting interesting research into networked drone technology. 13 , 14 The solution has high autonomy and complex local interactivity:

An example of simple local interactivity and low autonomy is an IoT solution with temperature sensors. The sensor measures the temperature at intervals and

sends text data to the cloud. This data is often aggregated later into a dashboard which can be viewed

on a mobile phone or PC. A network of drone cameras can be deployed to cover live events, such as high-action sports An example of complex local interactivity and low game played on a large field, but managing autonomy is an industrial IoT solution in a large networked drone cameras in real-time is factory. Such a solution may have thousands of sensors challenging. Distributed approaches yield that monitor machines. It has low autonomy because suboptimal solutions from lack of coordination the sensors are not processing much data. Nor are they but coordination with a centralized controller making decisions at the edge. The local interactivity is incurs round-trip latencies of several hundreds of complex because sensors and devices on the milliseconds over a wireless channel. We propose manufacturing line need to communicate and organize a fog-networking based system architecture to in real time. automatically coordinate a network of drones

equipped with cameras to capture and broadcast Virtual assistant smart speakers, such as Amazon Echo, the dynamically changing scenes of interest in a Google Home, and Apple HomePod are examples of sports game. We design both optimal and products with low levels of local interactivity and low practical algorithms to balance the tradeoff autonomy. The user asks one (or at most a few) between two metrics: coverage of the most question that the device responds to in short order. important scenes and streamed video bitrate. To This is indicative of low interactivity. Autonomy is low compensate for network round-trip latencies, the because most or all of the compute and algorithms are centralized controller uses a predictive approach done on a centralized server and sent back to the to predict which locations the drones should device. cover next. The controller maximizes video bitrate by associating each drone to an optimally

matched server and dynamically re-assigns drones as relay nodes to boost the throughput in low-throughput scenarios. This dynamic assignment at centralized controller occurs at slower time-scale permitted by round-trip latencies, while the predictive approach and drones’ local decision ensures that the system works in real-time.” - Princeton Edge Lab

13 http://edge.whitegoosetech.com/research/fog- 14 https://ieeexplore.ieee.org/document/8396351 computing/networked-drone-cameras

DEVICE EDGE CHARACTERISTICS The following chart provides a checklist for categorizing device Edge systems.

DEVICE EDGE CONSIDERATIONS

Edge Description Homogeneous Heterogeneous

Local Interactivity Simple Complex

Autonomy High Low

Deployment Area Populated Unpopulated

Deployment Environment Open, line of sight, few barriers Closed, walls, buildings, objects

Deployment Space Public Private

Environment Friendly Hostile

Online - Status Always-on Intermittent

Number of Endpoints High Low

Endpoint Type Living Inanimate

Endpoint Movement Mobile Stationary

Endpoint Data Producer Consumer

Endpoint Data Type Text, descriptive Binary

Bandwidth Requirements High Low

Reliance on Low Latency High Low

Data Processing at Edge High Low

Data Processing at Core High Low

Data Passed to Central Server High Low

Data Transfer Transactional Best Effort

Data Transmission Redundancy High Low

Intelligence - Algorithms Narrow AI Conditional (if/then)

Intelligence - Situation Changing Fixed

Endpoint Duration Permanent Temporary

Examples Person, Animal, Plant Machine, Building, Device, Street, Bridge

Security Simple Complex

Integrated Risk Management Simple Complex

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PLANNING YOUR STRATEGIC EDGE INITIATIVE

The purpose of this section is to help you plan your Generally speaking, to be most effective, project types strategic Edge initiatives. Because emerging tech with higher numbers require components that fall projects are built with rapidly maturing technologies, it earlier on their life cycles. Once you have an idea of is most effective to begin with a vision for how your what types of Intelligent Edge projects you are key components are likely to mature over time. This is considering for your organization, it can be useful to especially true if you intend to gain a competitive decompose the solutions into technical components. advantage from your efforts. These components can then be mapped on their life cycles. Next, a plan can be developed that anticipates As mentioned in an earlier section, the type of project maturity timelines. you are undertaking is an important consideration. The four project types are: The following section explains how to use life cycles to enhance your strategic plan. In the second section,  Improvement of operational efficiencies or since Edge projects often produce large amounts of reduction of expenses. data, we explore data management at the Edge. In the  Scaling a current line of business. third section, we explore AI, specifically Deep Learning,  Entering new markets. and look at business cases in order to develop a better  Innovation or development of a new product understanding of what types of AI can be applied to or service. business problems at the Edge.

INTELLIGENT EDGE LIFE CYCLES Often, the key components in Intelligent Edge systems are emerging technologies. Yet, sometimes this is not the case. For example, mobile devices are mature Edge products. Intelligent Edge features of the iPhone, such as Face ID, have high adoption rates and a sophisticated supply chain. This is a new feature differentiation, but it is not an emerging technology.

An important step in developing a strategy is to distinguish emerging Edge technologies from other types of technologies. To begin, let’s consider the life cycle S-curve graph below which approximates the Of course, real consumer adoption is never smooth. As current stage of Intelligent Edge systems. an example, consider the number of iPhones sold per The technology life cycle curve correlates to its quarter in the four years following its original release consumer adoption curve. In fact, it is the integral of in 2007. it. The adoption curve was developed through research done by Everett Rogers in 1962. Geoffrey Moore popularized it in his 1991 book Crossing The Chasm. Moore argues there is a chasm between visionaries and pragmatists. The Intelligent Edge solutions that have the most potential for strategic value fall approximately where Moore has defined this chasm.

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generated imagery over real world objects. Current revenues are in the hundreds of millions, but valuations for the company exceed $6B.

In contrast to commodity products, emerging technologies have:

 Relatively high production costs  Low number of users  Incomplete and/or inconsistent technology which is not robust  Complex manufacturing processes -- often they must be custom-built from start to finish  Higher margins iPhone units sold per quarter Innovations source: wikipedia commons Innovations precede emerging technologies by One critical step in the planning process, therefore, is some period of time, sometimes years, decades, or to assess whether the key components required to centuries. A current example of an innovation is the build your Edge system are commodities, emerging biotechnology that produces high-fidelity stem cells. technologies, or innovations. Each is discussed below. This process involves removing human stem cells, sorting them by fidelity, and then injecting a Commodity Products percentage of the cells back into the body. This treatment can cure certain diseases, such as sickle cell Technologies in commodity markets that are anemia, and may reverse aspects of aging. nearing or past 80-90% adoption rates (upper right of technology life cycle curve) have the following This stem cell therapy approach has been proven features: scientifically in separate stages. It will likely be effective as disease treatment in time. But the  Relatively low production costs manufacturing process is complicated. It has yet to be  High number of users solved even for low production numbers. Engineering  Robust, feature-rich technology offerings a solution that keeps the cells alive long enough to  Efficient, streamlined, and disaggregated complete the process has proven difficult. Currently, manufacturing processes the process is conducted by single teams of  Low margins researchers. These scientists use generic lab Current examples of commodity products include equipment and manually sort individual cells. HDTVs, toasters, washing machines, automobiles, and All one needs to do to understand the difference microwaves. between an innovation and an emerging Emerging Technologies technology is to consider the difference between Magic Leap and sorting stem cells. These may appear An example of an emerging technology is the next to each other on the life cycle curve, but they augmented reality (AR) product developed by Magic require qualitatively different approaches to bring to Leap. Magic Leap One is a wearable that enables users market. to interact with digital devices in a visually cinematic way. The product superimposes 3D, computer- An example of a technology that has run its course from innovation to commodity product is the gas-

powered automobile. Over time, incremental CONSIDERING SUPPLY CHAIN improvements were made not only to the product but DISAGGREGATION also the manufacturing process. These improvements allowed production to scale and helped democratize An inefficient and unsophisticated supply chain is one access to automobiles worldwide. of the demarcating factors of early markets. Building a product in an early market requires a far more Considering Maturity & Adoption extensive effort and a broader level of expertise than building one in a mature market. On average, consumer adoption occurs faster today than it has in the past. The following chart Consider shipbuilding. Early shipyards, which date back depicts how long in years certain technologies took to at least to the 4th millennium B.C., started the process reach 90% adoption: with wood and other raw materials. It required an enormous amount of effort, planning, and expertise to turn trees into ships.

By contrast, modern shipbuilding employs almost entirely prefabricated sections. Known as “block construction,” completed multi-deck segments of the hull or superstructure are built elsewhere and then transported to the building dock and lifted into place. Equipment, pipes, electrical cables, computers, electronics, and other components within the blocks are preinstalled. Most of the components used to assemble large construction blocks are purchased in massive, secondary markets. Those markets supply Speed of adoption has become extremely fast in recent hundreds of thousands of SKUs that go into a years. This is especially true for software and software- completed ship. Modern production is also difficult, but defined replacements for hardware. APIs, for example, the effort goes into scale and pace. are being rapidly developed and adopted. This breaking up of the process of manufacturing into Research indicates that adoption rates are influenced smaller components or elements for which new by five factors. 15, 16: markets then evolve is called disaggregation. The web and mobile application supply chain has  Perceived relative advantage: Does the undergone a disaggregation similar to the shipbuilding technology provide some sort of clear industry although the change happened in 10 years advantage? rather than 7,000.  Ease of compatibility: How easy is it to assimilate the technology into current In the early 2000s, the manufacturing process of processes? developing an application primarily consisted of  Low complexity: How easy is it to configure custom-building an entire app from start to finish. the technology and get working? Application deployments contained mostly custom  Trialability: Can the technology be tested code sometimes built on top of web application easily? frameworks, such as Spring MVC, JSF and Struts.  Observability: Can others easily observe the Ancillary files that provided third-party code called technology? “archived files” (e.g., .jar and .war files) were included

15 https://www.amazon.com/Diffusion-Innovations-5th-Everett- 16 http://itidjournal.org/index.php/itid/article/download/1423/524 Rogers/dp/0743222091

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with deployments to complement custom code. Unlike Blockcypher: Enables companies to easily build APIs, however, they were coupled to application blockchain applications with web APIs and callbacks. versions and were clunky to deploy and maintain. These ancillary files are similar to embedded Edge APIs Import.io: Web Data Integration solution extracts, which are discussed later in this section. prepares, and integrates high-quality comprehensive web data into customers' analytics platforms and Today, by contrast, web and mobile software products business applications. rely on an average of 10-20 external APIs per application. There are thousands of APIs available. A Twilio: Handles messy telecom hardware and exposes few popular examples include: a globally available cloud API that developers can interact with to build intelligent and complex Stripe: Online payment processing. communications systems.

Amazon Rekognition: Uses AI to identify the Since application development has matured around a objects, people, text, scenes, and activities in photos centralized computing approach, this secondary and videos. market for APIs has emerged to outsource activities that go into developing applications such as SendGrid: Delivering your transactional and registration, SMS messaging, credit-card processing, marketing emails through the world's largest cloud- and algorithm processing. The application market does based email delivery platform. this in the same way that automobile and ship manufacturing companies outsource pre-assembled Full Contact: From an email address or social media components. identifier, provides a full user profile including name, age, location, gender, and social network accounts. The disaggregation of traditional application development is evidenced not only by the existence of Okta: Adds authentication, authorization, and user the growing API market but also next generation API management to your web or mobile app within management companies, such as Google Apigee, minutes. MuleSoft, and Kong. These companies act as API gateways, manage numerous APIs, and handle Twitter API: common issues such as logging, authentication and setting rate limits. Facebook API:

IBM Watson: Mostly NLP AI for business -- itbuilds THE EDGE SUPPLY CHAIN HAS NOT YET models and develops applications to make more DISAGGREGATED accurate predictions, automate processes, interact with As the current API and SaaS market has grown around users and customers, and augment expertise. traditional, centralized software and mobile application development, it makes sense that a new model, Edge Newscred: Provides brands and publishers with computing, would require a different type of access to fully-licensed articles, images, and video from outsourced components. Indeed, it does. more than 2,500 world-class sources. Traditional APIs do not work for Edge use cases. These Uipath: Robotic software quickly automates repetitive APIs are deployed on centralized servers and accessed processes. from applications running on PCs, mobile devices, and other nodes. This approach is antithetical to the core Algorithmia: Deploy AI at scale; API that exposes the benefits of Edge computing. collective knowledge of algorithm developers across the globe.

INTELLIGENT EDGE BENEFITS CENTRALIZED API ACCESS

Handles large volumes of complex data Too much data to transmit

Reduces latency Increases latency

Lowers bandwidth requirements Increases bandwidth requirements

Increases autonomy Reduces autonomy

As an example, consider the Edge business case of a of work. Embedded APIs are similar to archive files in camera that is conducting real-time license plate that they are coupled, one-to-one, per deployment. identification on an interstate. Testing indicates that if Since Edge data centers can offer sub-10-millisecond a per-image response time of less than 10 milliseconds response time, they may be a viable deployment model is achieved, the system can correctly identify up to to make traditional APIs available to Edge applications. 98% of the plates. A cloud API, such as AWS Rekognition, has a baseline network latency of 70 milliseconds. Additional time to process and send the payload is also required. Clearly, in this scenario, a cloud-based API solution will not work.

In order to meet the engineering requirements of this application, the AI algorithm that reads the license plate needs to be moved closer to the camera. In other words, Edge applications need a different infrastructure model for disaggregated edge components which we will call ”Edge APIs” for lack of a better term.

Edge APIs can be deployed:

 In an Edge or micro data center (Edge data centers can have sub-10 millisecond response times and micro data centers are typically even faster);  On a processor within local proximity -- AWS Greengrass on a nearby server, for instance;  On the camera via an embedded API.

Currently, most commercial license plate cameras use embedded APIs or embedded custom code. This means that a local copy of the API is kept on the camera which, of course, means that you no longer have the benefits of a centralized API; namely, a single, managed access point for transacted, predefined units

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addition, markets consolidate and prices become lower. These factors can be used to help map where components fall on the curve and where they will be in the future. This helps keep your solution set current and to provide future strategic advantages.

The previous sections have discussed what the technology life cycle and adoption curves look like as well as how supply chain disaggregation and market forces can affect your Edge solution. To start, technologies fall into four primary life cycle stages Intelligent Edge applications, such as industrial IoT shown below: applications running AI at the edge, are typically mostly custom-built, much like the web applications from twenty years ago. Other components of Intelligent Edge applications, however, do have sophisticated supply chains. AI chips borrow manufacturing techniques from fab facilities that have been honed over decades. Custom-built AI chips are relatively new, but AI has been augmented by coprocessors since the early 1990s. Apple’s A12 and new A13 are manufactured by TMSC (Taiwan Semiconductor Manufacturing Company, Limited) and are produced in high-tech manufacturing facilities. The chips are Many proposed Edge systems have components that designed to work with state-of-the-art AI and deep fall into each one of the last three stages: strategic learning algorithms which also makes the output new advantages, products, and commodities. It is important and powerful. Similarly, microcontrollers, sensors, and to differentiate components by stage and create a actuators used in IoT devices are mature, decades-old roadmap and budget that reflects how the components markets with staid manufacturing processes. are likely to evolve. As components mature, costs Mapping Your Edge Components become lower, the supply chain disaggregates, and markets consolidate. Planning to adopt new The goal of mapping Edge components is to determine technologies as they move from innovations to custom- approximately where they fall on their life cycles and built systems and early products can maintain a how fast they are maturing and being adopted. This competitive advantage for strategic initiatives. allows you to anticipate and plan for how technologies are likely to change over time, which components are As an example, consider a simple IoT business case strategic assets, and how to build long-term roadmaps where a company that manages forklifts would like to that provide strategic advantages. measure how its fleet is loading pallets. The company would like to have sensors on its forklifts that identify As technologies mature, associated supply chains tend pallets via RFID and also provide a centralized to disaggregate and become more sophisticated. In dashboard that reports real-time loading information.

EDGE DATA MANAGEMENT The amount of data generated at the edge will be far over time and provides a high net return on too large to handle centrally. So it is imperative to investment. make good data management decisions at the edge. As data becomes the most valuable technology asset The goal of a Data Strategy is to produce a long- in organizations, deciding which data to store and how term asset that appreciates over time and provides to store it will require a solid methodology. Integrating a high net return on investment. this methodology with the organization's overall strategy requires thoughtful planning. There are many ways to measure the ROI of a data STRATEGIC EDGE DATA DESIGN strategy. One emerging discipline is Infonomics. Infonomics is the practice of applying accounting Proprietary data assets provide the best long-term principles to data and managing it as a company asset. strategic advantage. Emerging technologies, such as Less formal methods can be useful too. For most IoT, AI, and ML can provide short-term advantages. organizations, how you measure the value of data does Over time, however, technologies mature and become not matter as much as creating a plan that you can necessary costs of doing business. Intellectual property execute and then sticking to it. can provide defensibility, but it is often ineffective for software. To help with this process of building an Edge data strategy, I developed a quick, useful model to get Data is the one technical asset with which your efforts pointed in the right direction. The model has 3 business can form a mutually beneficial symbiotic stages. relationship. When properly designed, data becomes more useful and valuable over time. What other assets First, the organization’s strategy is considered. The have this characteristic? data and the design of the data should align with the goals of the company. This may sound simple, but is Consequently, building defensible data assets is more too often an afterthought. important than ever. It has become easier to collect data to train AI and ML models. Growing sources of Second, the data should be collected in a way that is free data sets are readily accessible and well organized. fit for experimental modeling. This allows a range of Other data is also available through APIs and data stakeholders to probe the data for insights. brokers. Third, for strategic data that is being used to build a Edge applications can produce massive amounts of company asset (not all data is strategic), the data data -- far too much to store or process -- so it is critical should be defensible/excludable. This means that it is at the outset to have a categorical Edge data strategy. difficult or impossible for competitors to replicate the Edge data strategies differ from centralized data data or gather similar data that provides the same strategies in tactics and engineering, but the goal is the insights. same -- to produce a long-term asset that appreciates

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STRATEGIC DATA DESIGN

Aligned to Fit for Defensible/ Organizational Experimental Excludable Strategy Modeling Data

The data and design is The data and design allows Data collected/produced is aligned to organization’s for data scientists and difficult or impossible for strategy and possible statisticians to test valid others to copy or future scenarios hypotheses reproduce

1. DATA IS ALIGNED TO ORGANIZATIONAL can be analyzed to derive site intelligence. The software is collaboration-focused and allows maps to STRATEGY be shared across team members. Defining data collection and design that aligns with and provides insights into the formation of organizational The software, however, can also be used in a variety strategy is the first step. Data should be robust to an of industries such as solar energy, agriculture, organization’s strategy and possible future scenarios. insurance inspection, construction, and more. For If the data/design allow stakeholders to test and example, in the agriculture industry, DroneDeploy can explore a range of viable strategic options at the be used to efficiently examine crop yield and minimize business and corporate strategy level, then it is more crop loss. For insurance claims, DroneDeploy can be than just a closed-loop function of IT that is specific to used to take overhead shots of the site, which can be some operational or similar process. used in claims analysis.

An example of an Intelligent Edge company that This allows Drone Deploy to branch into a number of collects data providing numerous strategic options is data verticals and provides strategic business DroneDeploy. DroneDeploy is a drone and UAV opportunities to test markets, provide APIs, partner, or mapping platform primarily used to capture job sites. license. Users are able to automate flights using a mobile app, capture geotagged imagery, and generate maps that

2. DATA FIT FOR EXPERIMENTAL The key is to design the data in such a way that MODELING stakeholders — programmers, data scientists, statisticians, analysts, salespeople, managers, and When data is poorly designed or siloed, extracting executives — can freely create and test a range of valid value from it is difficult. This has less to do with hypotheses across datasets. Ideally, data is designed technical design than with experimental modeling. to allow for meaningful experimental modeling. Data design can be technically pristine, follow all the Consider the following from Data Scientist Brian Caffo best practices, and still be coupled to its primary at Johns Hopkins: purpose and inflexible for use in other ways.

BAD DATA SCIENCE SITUATION IDEAL DATA SCIENCE SITUATION17

Weak or no hypothesis Clearly defined data hypothesis specified a priori

Same data to form hypothesis is used to interrogate Access to rich and varied data sets in various phases hypothesis of design

Limited access to experimental design Control of experimental design

Retrospective data or only observational data (not Data is robust, complete. This includes A/B testing, random) randomization, and stratification can interrogate Hypothesis.

Population is wrong Clean data.

Sparse or proxy data Random sample data

Fragile conclusions Clear conclusions

Unclear decisions Decision is obvious

Opaque knowledge Parsimonious knowledge

An example of a company that collects data fit for distance because skin reddens slightly during experimental modeling is Neurodata Lab. Neurodata heartbeats. Lab provides AI-based solutions for real-time emotion analytics and analysis of consumer behavior in retail, Because computer vision data is so voluminous and banking, insurance industries, HoReCa (i.e., service could be used in a variety of ways, NeuroData Lab must industries), and service robotics. The solution analyzes carefully consider how its data is collected, stored, and facial expressions, vocal affects, body poses, analyzed. The data that NeuroData Lab collects interpersonal distance, gestures, respiration rate, and undergoes careful experimental design that is being other variables to analyze human behavior. Notably, continuously improved to ensure that the algorithms the camera can detect skin redness subtly enough to are accurate, not biased, and handle the normal range determine a person’s heart rate at a reasonable of human expression.

17 Source: Brian Caffo, PhD, Professor, Johns Hopkins University

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3. DATA DEFENSIBILITY/EXCLUDABILITY  Is unique to predictable, future situations that are difficult for others to reproduce; If anyone can obtain or reproduce a dataset, it is  Has high dimensionality (many attributes) and difficult to create value from it. Defensible data is breadth (a range of possible values for difficult or impossible for others to copy or reproduce. attributes); There are a number of dimensions to defensible data.  Is not perishable (remains valuable and These include data that: pertinent for a long time);  Is highly perishable but has a consistent future  Requires unique, proprietary knowledge to stream; produce or interpret;  Has a positive feedback loop -- in other words,  Requires unique knowledge and expertise to the data becomes more robust and valuable as scrub and prep; more of it is gathered.  Is unique to a brand;  Is time and/or location sensitive and is being As an example of defensible data, consider the collected by only one party; company CardioSecur. CardioSecur is a mobile ECG  Is unique to proprietary situations; that works by placing your mobile device on your chest.

It is able to determine if your current symptoms require Because the app is convenient when an ECG is not immediate medical attention. This software is used by readily available, it provides historical data that other individuals as a preventative measure to keep track of companies, even physicians and hospitals, cannot their heart health. It can also be used by users who easily reproduce. It can also be analyzed later to have suffered a heart attack and want to stay in-the- predict medical outcomes and for other purposes. know, in order to avoid further heart damage. The software is also able to track recurring symptoms after Only one company had access to John Smith’s ECG treatment for arrhythmias and atrial fibrillation which data on March 10, 2019 at 2:45pm E.T. Every data can help prevent stroke. point gained adds to a defensible data set for CardioSecur. Patients, physicians, hospitals, Users can employ the CardioSecur mobile app as a part pharmaceutical companies, and other entities could all of their daily routine to keep control over their heart benefit from more consistent ECG history for a broader symptoms and potentially prevent further range of patients. complications. It can also be used by those with a known family history of heart issues. Results can easily be shared with a physician.

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EDGE AI It is no secret that AI, specifically, deep learning, is the  1969-1970: Fall of connectionism. enabling layer for a new generation of Intelligent Edge  1988: The Strategic Computing Initiative applications. For many years, AI in the commercial canceled AI spending. sphere did not work that well. But since 2010, one  1990s: Fifth Generation computer project’s particular subset of AI— deep learning— has matured goals ended. into a set of useful, commercial products that address practical business problems. More recently, starting around 2010, another AI boom occurred with Deep Learning (“DL”). DL, a type of In this section, we are going to briefly explore the AI machine learning that is a subcategory of AI, is perhaps story and examine how AI can be used at the edge. the biggest AI breakthrough in history Most of the We will explore what types of algorithms and use cases commercial AI applications today are DL systems. are working, and what is not. You are already using DL although you may not realize A BRIEF HISTORY OF AI it since it’s not usually advertised as such. Deep learning examples include: In the summer of 1956, a group of researchers came together at Dartmouth University to brainstorm about  Google photo app (DL) how computers could be programmed to behave like  Buzzfeed headlines (tuned with DL) humans. This meeting, called The Dartmouth  Airbnb pricing (DL) Workshop, included Marvin Minsky, Nathaniel  Pinterest visual search (DL) Rochester, and Claude Shannon, among other famous  Facebook chat M app (NLP is DL) scientists. The event lasted eight weeks and serves as the foundation of what we currently think of as AI. In from three to eight years we will have a

It is interesting to note that what we call Artificial machine with the general intelligence of an Intelligence might be called Cybernetics today, but average human being. I mean a machine that will

the organizer of The Dartmouth Workshop, John be able to read Shakespeare, grease a car, play office politics, tell a joke, have a fight. At that McCarthy, did not wish to get into debates with the argumentative Nobert Wiener who was the leading point the machine will begin to educate itself with fantastic speed. In a few months it will be at expert in Cybernetics at the time so McCarthy chose the more generic term “artificial intelligence.” genius level and a few months after that its powers will be incalculable. -- Marvin Minsky, 1970 Since the 1950s, AI research has grown in fits and starts. There have been times when AI seemed to be making excellent progress. During these times, super intelligence seemed imminent: The software ecosystem supporting deep learning research has been evolving quickly, and At other times, called AI Winters, research languished has now reached a healthy state: open-source and little progress was made. The two largest AI software is the norm; a variety of frameworks are winters were 1974–1980 and 1987–1993, but there available, satisfying needs spanning from have been a number of others. Some examples exploring novel ideas to deploying them into include: production; and strong industrial players are backing different software stacks in a stimulating  1967 to 1976: The quiet decade of machine competition. - Pascal Lamblin on behalf of

translation. Yoshua Bengio

CATEGORIES OF AI emotions from text;(e.g., CloudFactory’s NLP engine); There are three basic categories of AI:  Components in smart cars(e.g., Tesla Model X);  Narrow AI or Weak AI. Narrow AI is pro-  Drone delivery (e.g., Walmart and Amazon). grammed/trained to perform a specific task.  Strong AI or Artificial General Intelli- When building business cases, it is important to gence (“AGI”)): Theoretical application of AI understand what Narrow AI does well and where it has that equals/emulates human intellectual activ- limitations. There are specialized tasks where AI ity by being able to address a broad array of performs better than humans and there are tasks problems. where AI struggles to compete with humans or which  Super Intelligence or Super AI: Theoreti- are not yet ready for commercial applications. cal AI that exceeds human abilities and other smaller episodes. The Electronics Frontier Foundation (“EFF”) maintains an up-to-date list that provides the status of AI DL is a type of Narrow AI. Contrary to popular media research for a range of tasks. This can be an excellent reports, Narrow AI is the only AI that exists today. resource for verifying the feasibility of projects as it Narrow AI can do amazing things, but in many ways it gives specifics on how AI algorithms currently perform is still fairly limited. There is not a definitive approach on certain tasks. You can also get a sense of the even within research for AGI, so it is not likely that it progress that is being made over time on a particular will emerge any time soon notwithstanding commercial task. As chips get faster and algorithms improve, you efforts to make it happen. Microsoft recently can expect steady improvements in this arena. announced that it has invested $1B into OpenAI in an effort to produce Strong AI applications within Azure. As an example, image classification that provides a set 18 To date, however, there are no AGI models or of images of various objects using CIFAR began functional Strong AI applications. exceeding average human capabilities in late 2014. 19 Algorithm performance for this task is mapped below: Narrow AI does a number of specialized tasks well. Here are common examples where AI is currently (or soon to be) commercially available.

 Apple’s Siri;  Amazon Alexa;  Amazon’s product recommendation engine;  Image recognition, such as facial recognition(e.g., AWS Rekognition);  Sentiment analysis,i.e., determining Source: https://www.eff.org/ai/metrics

18 https://www.tractica.com/artificial-intelligence/microsofts-openai- 19 https://en.m.wikipedia.org/wiki/CIFAR-10 deal-puts-spotlight-on-access-to-high-end-ai-compute/

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However, algorithms performing Visual Question For Edge applications, AI has two critical components- Answering (which involves recognizing events, - algorithms and chips (see earlier sections for relationships, and context from an image) do not yet companies related to these markets). AI accelerators perform at human levels. The best algorithms perform are specially designed as hardware acceleration for at 70% accuracy, whereas humans perform at 95%. artificial intelligence applications, especially artificial The image below depicts the type of problem that is neural networks, machine vision and machine learning. tested for this task. As this emerging hardware market matures, chips will be developed for specific tasks, notably DL tasks and algorithms.

The viability of Edge business cases that require AI depends in no small part on both the cost and functionality of chips and algorithms as they pertain to particular tasks. Understanding how these two markets are developing can be useful when developing strategic roadmaps.

Since AI tasks can be abstract, it can be useful to look at a range of practical applications to get an intuitive feel for which types of AI business cases are working and which are not. The following section contains a list of AI companies and applications with a practical explanation of what each one does.

AI COMPANIES

This section covers several companies that are using Here are a number of AI tasks for which the EFF AI to tackle business problems today. The name of compares algorithm and human performance: each company is followed by a brief description of how AI is being applied to real-world problems.  Written language;  Reading comprehension; Affectiva  Language modelling;  Conversation;  Translation; “Developer of an emotion-recognition software  Spoken language; designed to analyze subtle facial and vocal  Speech recognition expressions to identify human emotions. The  Music information retrieval; company's software uses computer vision, machine  Instrumentals tracks recognition; learning, and deep learning methodologies to train  Scientific and technical capabilities; algorithms that classify emotions and analyzes  Solving constrained, well-specified technical complex and nuanced human emotions and problems; cognitive states from face and voice, enabling  Reading technical papers; creators of digital experiences to build stronger  Solving real-world technical problems; connections with their users in an engaging,  Generating computer programs from interactive and effective manner.” - Pitchbook specifications;  Answering science exam questions

 Learning to learn better.

AI Automated Insights The AI has practical use cases such as building mobile applications targeted towards a fitness-conscious user Automated Insights specializes in natural language base and including workout tracking and caloric intake generation (NLG) software that creates readable calculation. Another use case is building a heart rate narratives from data. The company’s platform, and stress tracking app on the Azumio platform. Wordsmith, ingests enormous amounts of data and uses algorithms and rules to produce human-readable Bowery prose. Wordsmith has been described as a "a sort of personal data scientist, sifting through reams of data Agriculture. Plant yields. Uses vision recognition, that might otherwise go un-analyzed and creating multivariate optimization, robotic controls custom reports that often have an audience of one."1 The service works by ingesting structured data, Bowery Farming is applying AI to agriculture in analyzing it for insights, and then writing out those innovative ways. It uses AI, lights, robotics, cameras, insights in human-friendly prose. and its own operating system (BoweryOS) to enhance indoor farming. Bowery farming maintains high-tech Applied Intuition warehouses to help control exactly how plants grow. The software collects data from cameras and other Applied Intuition allows you to simulate autonomous sensors and uses AI to alter and control the vehicle software. It brings real-world maps into the environment to help plants grow. equation so that testing is practical. Objects in the environment are also included, making testing accurate Cardiogram and realistic. In addition to testing your software against typical examples, you’re able to create more Cardiogram is a health and fitness application that complex scenarios, which can account for Edge cases. provides hospital-quality ECG on your mobile phone. Software failures and improvements can also be easily Cardiogram develops a mobile application that provides tracked. heart rate data to predict and prevent heart disease. Cardiogram is using data science to detect the heart A company that is developing autonomous vehicle conditions and atrial fibrillation, all with a smart watch. software may use Applied Intuition for testing prior to Read how the technology works here. investing in expensive test vehicles and time- consuming trials. The software can also be used to CardioSecur track the validity of a new, proposed company CardioSecur is a mobile ECG that works by placing your expansion into the field of autonomous vehicles. mobile device on your chest. It is able to determine if Azumio your current symptoms require immediate medical attention. This software is used by individuals as a Axumio is a mobile health and fitness AI that offers preventative measure to keep track of their heart. data tracking for caloric intake, diabetes management, Alternatively, it can be used by patients who have and vitals checking (sleep, body weight, heart rate). suffered a previous heart attack and want to stay in- Their food recognition software works by analyzing a the-know, in order to avoid further heart damage. The photo of a food and returning nutritional information. software is also able to track recurring symptoms after The diabetes management software enables users to treatment for arrhythmias, and help prevent stroke. log food and track their blood glucose level. In addition to tracking, they offer the LifeCoin SDK -- a blockchain- Users can employ the CardioSecur mobile app as a part based system that rewards users for their healthy of their daily routine to keep control over their heart habits. symptoms and potentially prevent further complications. It can also be used by those with a

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known family history of heart issues. CardioSecur Deserve provides access to fair credit to underserved, results can easily be shared with a physician. but deserving populations. It creates more equitable credit products for young adults using deep learning. Cloudtag Deserve has recently partnered with H20.ai, an open- source AI company, to scale and enable faster Cloudtag has created the product Onitor, which offers deployment of Deserve’s proprietary algorithms. Onitor Track. Onitor Track is a wearable technology that tracks activity and heart rate. It provides custom Dialpad weight loss timelines so that people can take charge of their weight and health. The technology uses Dialpad is an automatic speech recognition software contactless sensors to monitor health information, and that analyzes conversations and returns analytics. The provides accurate data that can be interpreted and data is used to increase productivity and offer used.Onitor is usable by a mass audience concerned applicable decision-making insight. Dialpad works with personal health and wellness. Onitor Tracker will through the company’s business phone system offer a dashboard, which users can log into to see their (UberConference), inbound call center, and intelligent specialized data. sales dialer products.

Cresta The software can be used to improve customer service for sales teams by empowering them with an AI- Cresta helps sales agents double their sales enabled VoIP system. They can build rapport and save conversions by providing data relevant to their conversations to the CRM. Conversation analytics can conversations. It enables them to also provide help sales agents identity patterns and improve scripts. responses in quicker time, so that they can hold They offer specialized software for start-ups, small multiple conversations at once. The software turns businesses, and enterprise level clients. Businesses sales agents into experts and allows new team looking to move away from a PBX system and work members to ramp up quickly. globally or non-centrally will benefit.

This software can be used by a company to give its Doxel sales team the leverage it needs to spend more time making conversions and less time researching. It can Doxel is an AI that scans construction sites daily using also be used to manage and ensure that best practices autonomous robots to track progress and inspect are being used across the team. Cresta makes it so that quality, so that potential issues can be addressed salespeople do not rely on cookie cutter responses that immediately. They are able to provide figures for the make conversations impersonal. percentage of work completed, earned value statistics, and timeline progress reports. The software is able to Cygnon detect errors in construction that the human eye cannot detect. Self-driving software Construction sites can leverage Doxel during the build Databricks process to track real-time progress. This can be used as a tool to reduce potential liability due to poor DeepMap construction. Doxel can also alert site and project managers to construction problems as they arise so 3D maps for self-driving algorithms that they can be addressed before any further Deserve development interferes with their ability to fix the problem.

Drive.ai processes like inventory management, claims data accumulation, construction project planning, and Drive.ai is a self-driving vehicle AI that uses vision operations management. Kespry does not rely on users recognition, SLAM, and motion planning. The AI is two- knowing how to operate a drone. Flights can be fold. First, it is learning how to drive and how to planned, and data is returned as automated analysis. communicate with pedestrians. It prioritizes human safety. Consequently, their vehicles have panels that In the pulp and paper industry, Kespry can be display their intention to pedestrians like “waiting for leveraged for inventory taking and maximizing site you to cross.” While the vehicles are autonomous, they operations. The software is able to detect inventory can also be controlled by a remote operator as a last volume at great accuracy more safely, and while resort. sparing production team member time. Kespry also cuts out the need for third-party services to perform Drive.ai can be used as an on-demand ride hailing landfill biomass monitoring and other surveys. system, a new form of public transportation. This sort of transportation can be beneficial in areas of high Moov traffic, like around well-known destinations. Users are able to select where they will be picked up and where Moov is an audio fitness coach that guides you through they will go from a mobile app. a workout based on your movement and heart rate. The software encourages proper form and decreases DroneDeploy the potential for injuries. It can be used to track personal bests. Moov can be used for a variety of DroneDeploy is a drone and UAV mapping platform for movements and exercises like running, swimming, businesses to capture job sites. Users are able to cardio boxing, and circuit training. automate flights using a mobile app, capture geotagged imagery, and generate maps that can be The Moov software is usable through a hardware analyzed to derive site intelligence. The software is device that can be worn on the wrist or ankle while collaboration-focused and allows maps to be shared exercising. Users can improve their athleticism by across team members. tracking their progress through Moov. The software analyzes form in 3D, so is able to provide real-time The software can be used in a variety of industries such feedback on form, all which provide audio motivation. as solar energy, agriculture, insurance inspection, construction, and more. For example, in the agriculture OrCam industry, DroneDeploy can be used to efficiently examine crop yield and minimize crop loss. For OrCam is an AI with a wearable device that helps those insurance claims, DroneDeploy can be used to take with difficulty reading, vision problems, and blindness. overhead shots of the site, which can be used in claims The device sees what the user is seeing, and is able to analysis. discern text, faces, and products. It connects to Bluetooth to enable hands-free usability. OrCam Instacart recognizes gestures and voice commands. Best of all, it does not require an Internet connection. Using gradient boosted decision trees, deep learning with TensorFlow and Keras. VentureBeat Article The device can be used by visually impaired individuals to aid them in daily life activities like recognizing and Kespry announcing faces and colors, distinguishing bill money, and identifying grocery store products by barcode. This Kespry is a drone software for industry to gain aerial allows individuals to live more mobile and independent intelligence about job sites in a short amount of time. lives. This can be leveraged in a number of industries for

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PinDrop Sigopt

PinDrop uses its Phoneprinting™ technology based on This hyperparameter optimization solution automates machine learning to analyze more than 1,300 audio model tuning to accelerate the model development features on call center phone calls to reduce fraud. process and amplify the impact of models in production PinDrop creates a distinct telephony profile, while also at scale. This process empowers customers to generate revealing true geo-location, device type, etc. This more high-performing models in production. With more technology also works from IoT devices to provide models in production, they earn a higher return on security and identity voice protection. their modeling investment.

Primer Suki

Primer is a machine learning software product that Suki is a digital assistant for doctors that’s voice- takes data, gathers insight about it, and automatically enabled and alleviates administrative duties. It allows produces reports. The software is meant to meet the doctors to create accurate notes at a faster speed so demands of a world increasingly filled with information they can focus on other tasks. The software is data- that requires analysis. The AI is able to look at different secure and HIPPA compliant. Suki is also EHR components of the text including structure, ensemble, integrated, so notes can be seamlessly added, and event, context, differences, and story. patient information can be pulled easily. It also syncs across devices. Primer can be used for language automation where there is a need for quick and efficient processing of The software is used by doctors to increase time with documents for meaning. Some companies use patients and decrease time spent on administration. software like Primer to consistently examine product Suki addresses the needs of the 70% of doctors who markets and look for trends that can help streamline reported experiencing burnout by reducing their and reduce risk in their supply chain. administrative burden. As a result, doctors are able to cut hours off their work each week. Shield AI Skycatch Shield AI is a drone security Artificial intelligence that enables robots to see, reason about, and search the Skycatch is a drone technology that offers precise world. Shield AI develops AI systems for indoor and imaging. It’s able to export a multitude of data types outdoor intelligence, surveillance, and reconnaissance for accurate analysis. Automated routes can be started defense operations. Shield AI offers Hivemind, an AI from a mobile application. Skycatch uses a GPS base framework that enables machines, including station so that data can be analyzed in the field and in unmanned ground vehicles , unmanned aerial vehicles cases where there is no Internet connection. , unmanned aircraft systems , and unmanned underwater vehicles to learn from their real and The software can be used in industries that require synthetic experiences. Likewise, Nova is an imaging of a large area of land. With the app, you can autonomous quadcopter that enables access and plan and launch your flight, process the flight data, and exploration of buildings, dense urban environments, distribute the results. Data files can also be exported and areas lacking in global positioning system (“GPS”) to other software for further analysis. ) availability. Shield AI has developed a drone that can fly inside buildings using simultaneous localization and Textio mapping, detect faces, map interior spaces, and Textio is an augmented writing software that identify good and bad agents so that soldiers can be illuminates what your writing reveals about your better prepared to move into buildings. company culture, measures the impact of your words,

and increases productivity. Textio Hire uses scoring The software can be used by sales teams to increase and suggestions to improve job advertisements to sales performance with intelligent analysis and attract the best candidates for open positions. It uses suggested timing. It can also take strenuous and data about a company’s culture to align job postings repetitive tasks off the table for sales team members, with core values. It also calculates hiring scores that so they have more time performing more meaningful take into account geographic hiring data. work. Yseop builds out text on dashboards from charts, and can measure up performance with goals. Yseop Zipline Yseop uses AI for augmented analysis and sales. It reads text and distills it into actionable, easy-to-read Drone blood delivery. Zipline is an American medical information. Yseop automates report writing to product delivery company headquartered in Half Moon produce document summaries and recommendations. Bay, California. Zipline designs, builds, and operates The software also processes CRM data and gives small drone aircraft for delivery of medical products, suggestions to sales people about product matching for with a focus on providing services in Africa. The clients. The AI is also able to add narrative descriptions company operates two distribution centers in Rwanda to dashboards. and four in Ghana.

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EXECUTIVE INTERVIEWS

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AUTHOR

Daniel Sexton, Principal

Daniel Sexton is a Founding Principal at RedChip Ventures. Daniel has over 15 years of experience leading large-scale, technology solutions for Fortune 500 companies, such as Genuine Parts Company, CitiGroup, and Blue Cross Blue Shield. In addition, he has worked with a number of tech startups both as a founder and advisor. Daniel has several software certifications in Java and Cloud technologies.

Prior to founding RedChip Ventures, Dan was a Managing Partner at a private investment fund for 6 years where he helped lead and manage investments in technology and product companies.